Business Informatics and Mathematics (all)
Business Informatics (Bachelor)
Algorithms and Data Structures (Lecture)
DE
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
6
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
Die Studierenden kennen effiziente Algorithmen und effektive Datenstrukturen für grundlegende Probleme der Informatik und können diese anwenden und in Computerprogramme umsetzen. Sie beherrschen weiterhin grundlegende Techniken des Entwurfs von Algorithmen und Datenstrukturen, sowie der Korrektheits- und Laufzeitanalyse von Algorithmen
Methodenkompetenz:
Die Studierenden können anwendungsrelevanten Berechnungsproblemen effiziente Algorithmen zuzuordnen bzw. diese entwickeln und
mittels dieser lösen.
Personale Kompetenz:
Die Studierenden können Berechnungsprobleme in Anwendungszusammenhängen identifizieren, sie formal spezifizieren und damit einer rechentechnischen Lösung zuführen. Sie können auf höherem Niveau abstrahieren und mit formalen Modellierungstechniken arbeiten.
Die Studierenden kennen effiziente Algorithmen und effektive Datenstrukturen für grundlegende Probleme der Informatik und können diese anwenden und in Computerprogramme umsetzen. Sie beherrschen weiterhin grundlegende Techniken des Entwurfs von Algorithmen und Datenstrukturen, sowie der Korrektheits- und Laufzeitanalyse von Algorithmen
Methodenkompetenz:
Die Studierenden können anwendungsrelevanten Berechnungsproblemen effiziente Algorithmen zuzuordnen bzw. diese entwickeln und
mittels dieser lösen.
Personale Kompetenz:
Die Studierenden können Berechnungsprobleme in Anwendungszusammenhängen identifizieren, sie formal spezifizieren und damit einer rechentechnischen Lösung zuführen. Sie können auf höherem Niveau abstrahieren und mit formalen Modellierungstechniken arbeiten.
Recommended requirement:
Examination achievement:
Schriftliche Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Matthias Krause, Linda Scheu-Hachtel
Description:
- Grundtechniken des Algorithmenentwurfs sowie der Laufzeitanalyse (Divide and Conquer, Greedyheuristiken, Dynamic Programming,…)
- Grundtechniken des Beweisens der Korrektheit von Algorithmen
- Sortieralgorithmen
- Hashing und hashingbasierte Algorithmen
- Advanced Data Structures
- Algorithmen für Suchbäume
- Graphalgorithmen (Tiefensuche, Breitensuche, Minimum Spanning Trees, Kürzeste-Wege-Algorithmen)
- Ausgewählte weitere Algorithmen (z.B. Pattern Matching, Automatenminimierung…)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
CS 560 Large-Scale Data Management (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire knowledge about methods and systems for managing large datasets and data-intensive computing.
Students will acquire knowledge about methods and systems for managing large datasets and data-intensive computing.
Methodological competence:
• Be able to judge, select, and use traditional or non-traditional data management systems for a given data management task
• Be able to solve computational problems involving large datasets
Personal competence:
• Study independently
• Presentation and writing skills
Recommended requirement:
Examination achievement:
Written examination, exercises
90 minutes
90 minutes
Instructor(s):
Prof. Dr. Rainer Gemulla
Description:
This course introduces the fundamental concepts and computational paradigms of large-scale data management and Big Data. This includes methods for storing, updating, querying, and analyzing large dataset as well as for data-intensive computing. The course covers concept, algorithms, and system issues; accompanying exercises provide hands-on experience. Topics include:
• Parallel and distributed databases
• MapReduce and its ecosystem
• NoSQL
• Stream processing
• Graph databases
• Parallel and distributed databases
• MapReduce and its ecosystem
• NoSQL
• Stream processing
• Graph databases
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Database Systems I (Lecture)
DE
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
Verständnis der Grundlagen der Datenmodellierung bzw. des Datenbankentwurfs und der Funktionsweise von relationalen Datenbankmanagementsystemen, insbesondere Anfragebearbeitung und Transaktionsverwaltung
Methodenkompetenz:
Abstraktion, Modellierung, Aufwandsabschätzung für Anfragen
Personale Kompetenz:
Verständnis der Rolle moderner Datenhaltung in einem Unternehmen
Verständnis der Grundlagen der Datenmodellierung bzw. des Datenbankentwurfs und der Funktionsweise von relationalen Datenbankmanagementsystemen, insbesondere Anfragebearbeitung und Transaktionsverwaltung
Methodenkompetenz:
Abstraktion, Modellierung, Aufwandsabschätzung für Anfragen
Personale Kompetenz:
Verständnis der Rolle moderner Datenhaltung in einem Unternehmen
Recommended requirement:
Examination achievement:
schriftliche Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Guido Moerkotte
Description:
Datenbankentwurf, Normalisierung, Anfragebearbeitung, Transaktionsverwaltung
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Database Systems I (Lecture)
DE
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
Verständnis der Grundlagen der Datenmodellierung bzw. des Datenbankentwurfs und der Funktionsweise von relationalen Datenbankmanagementsystemen, insbesondere Anfragebearbeitung und Transaktionsverwaltung
Methodenkompetenz:
Abstraktion, Modellierung, Aufwandsabschätzung für Anfragen
Personale Kompetenz:
Verständnis der Rolle moderner Datenhaltung in einem Unternehmen
Verständnis der Grundlagen der Datenmodellierung bzw. des Datenbankentwurfs und der Funktionsweise von relationalen Datenbankmanagementsystemen, insbesondere Anfragebearbeitung und Transaktionsverwaltung
Methodenkompetenz:
Abstraktion, Modellierung, Aufwandsabschätzung für Anfragen
Personale Kompetenz:
Verständnis der Rolle moderner Datenhaltung in einem Unternehmen
Recommended requirement:
Examination achievement:
schriftliche Klausur (90 Minuten)
Instructor(s):
Nazanin Rashedi
Description:
Datenbankentwurf, Normalisierung, Anfragebearbeitung, Transaktionsverwaltung
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
1
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Formal Foundations of Computer Science (Lecture)
DE
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
Die Studierenden beherrschen grundlegende für die Informatik rele-vanten Konzepte, Begriffsbildungen und wissenschaftlichen Arbeits-techniken aus Mathematik und Logik. Sie kennen weiterhin eine erste Auswahl an wichtigen Datenstrukturen und effizienten Algorithmen für grundlegende Probleme.
Methodenkompetenz:
Die Studierenden besitzen die Fähigkeit, informal gegebene Sachver-halte formal zu modellieren und die entstehenden formalen Struktu-ren bzgl. grundlegender Eigenschaften zu klassifizieren. Sie können weiterhin auf einem für Informatiker adäquaten Niveau gegebene Aussagen mathematisch beweisen.
Personale Kompetenz:
Die Studierenden besitzen ein Grundverständnis der för die Informa-tik wichtigen formalen Strukturen, Modelle und Arbeitstechniken. Sie können auf höherem Niveau abstrakt denken und formal modellieren.
Die Studierenden beherrschen grundlegende für die Informatik rele-vanten Konzepte, Begriffsbildungen und wissenschaftlichen Arbeits-techniken aus Mathematik und Logik. Sie kennen weiterhin eine erste Auswahl an wichtigen Datenstrukturen und effizienten Algorithmen für grundlegende Probleme.
Methodenkompetenz:
Die Studierenden besitzen die Fähigkeit, informal gegebene Sachver-halte formal zu modellieren und die entstehenden formalen Struktu-ren bzgl. grundlegender Eigenschaften zu klassifizieren. Sie können weiterhin auf einem für Informatiker adäquaten Niveau gegebene Aussagen mathematisch beweisen.
Personale Kompetenz:
Die Studierenden besitzen ein Grundverständnis der för die Informa-tik wichtigen formalen Strukturen, Modelle und Arbeitstechniken. Sie können auf höherem Niveau abstrakt denken und formal modellieren.
Recommended requirement:
Examination achievement:
Schriftliche Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Matthias Krause, Linda Scheu-Hachtel
Description:
- Grundlagen Aussagenlogik (Folgern, Beweisen)
- Mengen, Relationen, Abbildungen
- Grundlagen der Kombinatorik (Abzählen von endlichen Mengen, Abzählbarkeit)
- Einführung Graphentheorie
- Algebraische Strukturen (Halbgruppen, Gruppen, Homorphismen, Faktorstrukturen)
-
Grundlegende Berechnungsmodelle/
Endliche Automaten
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Integrated Information Systems (Wifo) (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Registration procedure:
This course does not have limited capacity. Registration via Portal 2 will be possible from August 15, 2023.
Please note that this lecture is accompanied by exercise classes, you can register for them via Portal 2.
Please note that this lecture is accompanied by exercise classes, you can register for them via Portal 2.
Learning target:
After attending the lecture, exercises and tutorials students are able to:
- model complex business processes based on popular modelling techniques
- discuss the requirements, characteristics and effects of integrated information systems in industrial companies, including complex process interdependencies
- complete basic tasks from different functional areas in a wide-spread integrated information system.
Recommended requirement:
Examination achievement:
Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Armin Heinzl, Dr. André Halckenhäußer, Tobias Maier
Description:
This course first outlines the basics of data and business process modelling based on wide-spread approaches such as entity relationship diagrams, event-driven process chains (EPC), and business process model and notation (BPMN). The remainder of the course then focuses on the use and purpose of integrated information systems across different functional areas in industrial companies. Finally, basics of management support systems such as business intelligence systems are addressed.
- Business Process Modelling
-
Application Systems in
- Research and Development
- Marketing and Sales
- Procurement and Warehousing
- Production
- Shipping and Customer Service
- Finance, Accounting, HR
- Planning and Control Systems
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Artificial Intelligence (Lecture)
DE
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
Ziele und Grundlagen der Künstlichen Intelligenz. Suchverfahren als universelle Problemlösungsverfahren. Problemkomplexität und Heuristische Lösungen. Eigenschaften und Zusammenhang zwischen unterschiedlichen Suchverfahren.
Methodenkompetenz:
Beschreibung konkreter Aufgaben als Such-, Constraint- oder Planungsproblem. Implementierung unterschiedlicher Suchverfahren und Heuristiken.
Ziele und Grundlagen der Künstlichen Intelligenz. Suchverfahren als universelle Problemlösungsverfahren. Problemkomplexität und Heuristische Lösungen. Eigenschaften und Zusammenhang zwischen unterschiedlichen Suchverfahren.
Methodenkompetenz:
Beschreibung konkreter Aufgaben als Such-, Constraint- oder Planungsproblem. Implementierung unterschiedlicher Suchverfahren und Heuristiken.
Recommended requirement:
Examination achievement:
Erfolgreiche Teilnahme am Übungsbetrieb
schriftliche Klausur (90 Minuten)
schriftliche Klausur (90 Minuten)
Instructor(s):
Dr. Christian Meilicke
Description:
- Problemeigenschaften und Problemtypen
- Problemlösen als Suche, Anwendung im Bereich Computerspiele
- Constraintprobleme und deren Lösung
- Logische Constraints
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Practical Computer Science I (Lecture)
DE
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor, Master
Language of instruction:
German
Credit hours 1:
6
Learning target:
Fachkompetenz:
Die Studierenden können selbständig Algorithmen zu vorgegebenen Problemen entwerfen und in Java, das im parallel laufenden Pro-grammierkurs I unterrichtet wird, objektorientiert programmieren. Methodenkompetenz:
Algorithmenentwurf, Bewertung von vorgegeben Algorithmen Personale Kompetenz:
Kreativität beim Entwurf von Algorithmen, Teamfähigkeit
Die Studierenden können selbständig Algorithmen zu vorgegebenen Problemen entwerfen und in Java, das im parallel laufenden Pro-grammierkurs I unterrichtet wird, objektorientiert programmieren. Methodenkompetenz:
Algorithmenentwurf, Bewertung von vorgegeben Algorithmen Personale Kompetenz:
Kreativität beim Entwurf von Algorithmen, Teamfähigkeit
Recommended requirement:
Examination achievement:
Studienbeginn ab HWS 2011:
Erfolgreiche Teilnahme am Übungsbetrieb
schriftliche Klausur (90 Minuten)
Erfolgreiche Teilnahme am Übungsbetrieb
schriftliche Klausur (90 Minuten)
Studienbeginn vor HWS 2011:
schriftliche Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Frederik Armknecht, Jochen Schäfer
Description:
Vom Problem zum Algorithmus, vom Algorithmus zum Programm
- Entwurf von Algorithmen: schrittweise Verfeinerung, Modularität, Objektorientierung (Klassenhierarchien, Vererbung), Rekursion
- Die objektorientierte Programmiersprache Java
- Einfache Datenstrukturen (verkettete Liste, Binärbaum, B-Baum)
- Modellierung mit UML: Klassendiagramme, Aktivitätsdiagramme, Zustandsdiagramme
- Einführung in die Theorie der Algorithmen: Berechenbarkeit, Komplexität (O-Kalkül), Testen und Verifikation von Algorithmen und Programmen
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Programming Lab I (Lecture)
DE
Course type:
Lecture
ECTS:
5.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
On-campus and online, live
Learning target:
Fachkompetenz:
- Gründliche Kenntnis der Basiskonzepte der Programmiersprache Java
- Verständnis des Konzepts der Objektorientierung
- Kenntnisse der algorithmischen Prinzipien Iteration und Rekursion
- Basiswissen über das Arbeiten unter einem Linux-Betriebssystem
Methodenkompetenz:
- Fähigkeit, Algorithmen zu entwerfen
- Fähigkeit, komplexe Algorithmen in Java ohne Einsatz importierter Methoden zu programmieren
- Fähigkeit, rekursiv zu programmieren
Personale Kompetenz:
- Eigenverantwortliches Arbeiten
- Teamfähigkeit
Recommended requirement:
Examination achievement:
Programmiertestate, Programmierprojekte, Programming Competence Test (180 Minuten)
Instructor(s):
Dr. Ursula Rost
Description:
Im Programmierpraktikum I werden grundlegende Kenntnisse der objektorientierten Programmierung auf Basis der Sprache Java vermittelt.
Die Studierenden werden von dieser Sprache vor allem folgende Grundmerkmale und Konzepte kennenlernen:
Die Programmierausbildung erfolgt auf der Basis des Betriebssystems Linux. Hierzu werden ebenfalls Grundkenntnisse vermittelt, die es ermöglichen, einfache Java-Programme zu entwickeln. Im Laufe des Kurses wird darüber hinaus eine einfache Entwicklungsumgebung eingeführt.
Die Studierenden werden von dieser Sprache vor allem folgende Grundmerkmale und Konzepte kennenlernen:
- Basiskonzepte der Programmierung: einfache Datentypen, Variablen, Operatoren, Anweisungen, Kontrollstrukturen
- Zusammengesetzte Datentypen (Felder)
- Das Konzept der objektorientierten Programmierung
- Klassen (Attribute, Methoden, Konstruktoren)
- Vererbung
- Pakete, abstrakte Klassen und Interfaces
- Java API und wichtige Hilfsklassen
- Ausnahmebehandlung: Exceptions
- Programmierung Grafischer Oberflächen mit Swing
Die Programmierausbildung erfolgt auf der Basis des Betriebssystems Linux. Hierzu werden ebenfalls Grundkenntnisse vermittelt, die es ermöglichen, einfache Java-Programme zu entwickeln. Im Laufe des Kurses wird darüber hinaus eine einfache Entwicklungsumgebung eingeführt.
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Programming Course (Lecture w/
EN
Course type:
Lecture w/
ECTS:
Course suitable for:
Bachelor
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Dr. Ursula Rost
Business Informatics I: Introduction and Foundations (Lecture)
DE
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
2
Attendance:
Live & on-campus
Registration procedure:
This course does not have limited capacity. Registration via Portal 2 will be possible from August 15, 2023.
Please note that this lecture is accompanied by exercise classes and tutorials, you can register for them via Portal 2.
Please note that this lecture is accompanied by exercise classes and tutorials, you can register for them via Portal 2.
Learning target:
Anhand der Vorlesung sollen Sie erlernen, dass Wirtschaftsinformatik mehr als die Nutzung von Informationstechnik ist. Die Inhalte werden Sie im weiteren Verlauf Ihres Studiums sowie bei der Verwertung des erlernten Wissens in ihrer Bachelorarbeit nutzenbringend verwerten können.
Recommended requirement:
Examination achievement:
80% Schriftliche Klausur (90 Minuten)
20% Gruppenarbeit
20% Gruppenarbeit
Instructor(s):
Prof. Dr. Armin Heinzl, Luis Oberste
Description:
Die Vorlesung Wirtschaftsinformatik I vermittelt die Fundamente der Wirtschaftsinformatik als wissenschaftliche Disziplin. Im Rahmen einer Einführung werden unter anderem der Gegenstand, der Wissenschaftscharakter, die Forschungsziele, -theorien, und -methoden sowie Nachbardisziplinen und ein Ländervergleich behandelt. Im Rahmen der Grundlegung werden zentrale Inhalte wie Informationsbedarf, Informationsverhalten, Informationssystem, Informationsinfrastruktur, Benutzerverhalten, Aspekte einer Entwurfslehre und Inhalte der Evaluationsforschung vermittelt.
Bitte klicken Sie hier für weitere Informationen.
Bitte klicken Sie hier für weitere Informationen.
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Business Informatics III: Development and Management of Information Systems (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Registration procedure:
This course does not have limited capacity. Registration via Portal 2 will be possible from August 15, 2023.
Learning target:
In order to be able to deal with these challenges, the “Development and Management of Information Systems” course is designed to introduce students to the various stages of the life cycle of an IS. Starting with the initial idea and conception of a system, the course will cover the process from development to introduction and, finally, application and value creation. In doing so, students will get to know the various entities and roles involved in IS development and management.
The primary objective of the course is to enable students to play a vital role at the intersection of technical and business issue, being able to bridge the gap between a company’s end users and IT experts. In doing so, they shall understand that IS transcend mere technological artifacts but constitute complex socio-technical phenomena.
To support students in their learning, the course will offer a basic introduction to the IS phenomenon, system types, and roles involved in development, introduction, management, and use of IS. Subsequently, each of these phases will be looked at in greater detail. For each phase, both the processes as well as at the contents of each domain will be introduced and discussed. Beyond the presentation of basic concepts, methods, and theories, the course will also provide students with opportunities to extend and practice their theoretical knowledge with interactive elements, an industry speaker, and a case study.
The primary objective of the course is to enable students to play a vital role at the intersection of technical and business issue, being able to bridge the gap between a company’s end users and IT experts. In doing so, they shall understand that IS transcend mere technological artifacts but constitute complex socio-technical phenomena.
To support students in their learning, the course will offer a basic introduction to the IS phenomenon, system types, and roles involved in development, introduction, management, and use of IS. Subsequently, each of these phases will be looked at in greater detail. For each phase, both the processes as well as at the contents of each domain will be introduced and discussed. Beyond the presentation of basic concepts, methods, and theories, the course will also provide students with opportunities to extend and practice their theoretical knowledge with interactive elements, an industry speaker, and a case study.
Recommended requirement:
Examination achievement:
Written exam (30%) (90 Minuten)
Case study write-up (70%)
Case study write-up (70%)
Instructor(s):
Prof. Dr. Hartmut Höhle, Jan Schilpp
Description:
During the last decades we witnessed a growing importance of Information Systems (IS) in the business world along with faster and faster innovation cycles. A case in point is the growing IS-related expenditure of corporations, forecasted to total EUR 2.63 trillion in 2012 – a 4.7% growth over 2011 (Gartner 2013). Ranging from the enrichment of routine working tasks (i.e., employee portals to integrate disparate applications, data, and processes (Daniel and White 2005)) to the e-enabled integration of entire business eco-systems (e.g., platform-based integration of supply chains (e.g., Kroenke 2010)), IS have become a vital backbone of businesses.
Consequently, the ability to use IS in a way supporting the overall value proposition of a corporation has become a central success determinant for many firms. Accordingly, the “Development and Management of Information Systems” course is designed to introduce students to the nature, role, and potentials of IS in corporations and enable them to serve as a meaningful interface between technology and business.
Once filling this role in a business context, the future IS professionals are likely to be facing two major trends: the increasing industrialization of IS (Brenner et al. 2007; Daberkow and Radtke 2008; Walter et al. 2007) and a shift towards service-orientation in IT organizations and processes (Hochstein et al. 2005; Roewekamp 2007). This brings about challenges such as, among others, managing the trade-off between efficient execution and effective offering or recognizing and mitigating conflicting expectations and goals among the many entities (i.e., software producers, consultants, corporate users, customers) and roles (i.e., business professionals, technical staff, corporate management) involved in an IS.
Consequently, the ability to use IS in a way supporting the overall value proposition of a corporation has become a central success determinant for many firms. Accordingly, the “Development and Management of Information Systems” course is designed to introduce students to the nature, role, and potentials of IS in corporations and enable them to serve as a meaningful interface between technology and business.
Once filling this role in a business context, the future IS professionals are likely to be facing two major trends: the increasing industrialization of IS (Brenner et al. 2007; Daberkow and Radtke 2008; Walter et al. 2007) and a shift towards service-orientation in IT organizations and processes (Hochstein et al. 2005; Roewekamp 2007). This brings about challenges such as, among others, managing the trade-off between efficient execution and effective offering or recognizing and mitigating conflicting expectations and goals among the many entities (i.e., software producers, consultants, corporate users, customers) and roles (i.e., business professionals, technical staff, corporate management) involved in an IS.
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Business Informatics (Master)
Advanced Methods in Text Analytics (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Simone Paolo Ponzetto, Dr. Daniel Ruffinelli
Advanced Software Engineering (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
After taking the course, students will be familiar with the latest state-of-the-art techniques for specifying the externally visible properties of a software system/
Participants will know how to use the expertise acquired during the course to describe the requirements that a system/
With the acquired skills and know-how, students will be able to play a key role in projects involving the development of systems, components and software applications.
After taking the course, students will be familiar with the latest state-of-the-art techniques for specifying the externally visible properties of a software system/
Participants will know how to use the expertise acquired during the course to describe the requirements that a system/
With the acquired skills and know-how, students will be able to play a key role in projects involving the development of systems, components and software applications.
Recommended requirement:
Examination achievement:
Written examination, 90 minutes
Instructor(s):
Prof. Dr. Colin Atkinson
Description:
The course deals with the model-based specification of software systems and components as well as their verification, validation and quality assurance. The emphasis is on view-based specification methods that use multiple views, expressed in multiple languages, to describe orthogonal aspects of software systems/
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Blockchain Security (Lecture)
DE
Course type:
Lecture
ECTS:
Course suitable for:
Master
Language of instruction:
German
Instructor(s):
Prof. Dr. Frederik Armknecht, Jochen Schäfer
CS 560 Large-Scale Data Management (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire knowledge about methods and systems for managing large datasets and data-intensive computing.
Students will acquire knowledge about methods and systems for managing large datasets and data-intensive computing.
Methodological competence:
• Be able to judge, select, and use traditional or non-traditional data management systems for a given data management task
• Be able to solve computational problems involving large datasets
Personal competence:
• Study independently
• Presentation and writing skills
Recommended requirement:
Examination achievement:
Written examination, exercises
90 minutes
90 minutes
Instructor(s):
Prof. Dr. Rainer Gemulla
Description:
This course introduces the fundamental concepts and computational paradigms of large-scale data management and Big Data. This includes methods for storing, updating, querying, and analyzing large dataset as well as for data-intensive computing. The course covers concept, algorithms, and system issues; accompanying exercises provide hands-on experience. Topics include:
• Parallel and distributed databases
• MapReduce and its ecosystem
• NoSQL
• Stream processing
• Graph databases
• Parallel and distributed databases
• MapReduce and its ecosystem
• NoSQL
• Stream processing
• Graph databases
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Data Mining (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Registration procedure:
Please note that there is no second date for the exam.
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of data mining. Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of data mining. Methodological competence:
- Successful participants will be able to identify opportunities for applying data mining in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project organisation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), project report, oral project presentation
Instructor(s):
Prof. Dr. Heiko Paulheim
Description:
The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:
- Goals and Principles of Data Mining
- Data Representation and Preprocessing
- Clustering
- Classification
- Association Analysis
- Text Mining
- Systems and Applications (e. g. Retail, Finance, Web Analysis)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
1
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Higher Level Computer Vision – Exercise (Lecture w/
EN
Course type:
Lecture w/
ECTS:
6.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr.-Ing. Margret Keuper
Higher Level Computer Vision – Lecture (Lecture w/
EN
Course type:
Lecture w/
ECTS:
6.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr.-Ing. Margret Keuper
IE 675b Machine Learning (Lecture)
EN
Course type:
Lecture
ECTS:
9.0
Course suitable for:
Master
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Rainer Gemulla
IS 613 Applied Project in Design Thinking and Lean Software Development (Lecture w/
EN
Course type:
Lecture w/
ECTS:
6.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
On-campus and online, live
Registration procedure:
This course has limited capacity. Applications are only possible by applying over our chair's application tool (accessible inside the university network or per VPN only). If you are applying for IS 613 and IS 615, one application is sufficient but indicate that you're applying for both courses. Only within the period listed below is an application possible!
Application period: 01.08.23 – 01.09.23
Requirements:
- Short cover letter (1–2 paragraphs) about your motivation and your previous experience in software development
- Curriculum vitae and study results (transcript of records)
Confirmations will be sent on Monday, 04 September 2023. Cancellation is possible until 10 September 2023.
Instructor(s):
Dr. Alexander Scheerer, Deborah Mateja
Description:
Please find a detailed course description via the following link:
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
IS 614 Corporate Knowledge Management (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Registration procedure:
This course does not have limited capacity. Registration via Portal 2 will be possible from August 15, 2023.
Instructor(s):
Deborah Mateja
Description:
Please find a detailed course description via the following link:
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
IS 615 Design Thinking and Lean Development in Enterprise Software Development (Lecture)
EN
Course type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
On-campus and online, live
Registration procedure:
This course has limited capacity. Applications are only possible by applying over our chair's application tool (accessible inside the university network or per VPN only). If you are applying for IS 613 and IS 615, one application is sufficient but indicate that you're applying for both courses. Only within the period listed below is an application possible!
Application period: 01.08.23 – 01.09.23
Requirements:
- Short cover letter (1–2 paragraphs) about your motivation and your previous experience in software development
- Curriculum vitae and study results (transcript of records)
Confirmations will be sent on Monday, 04 September 2023. Cancellation is possible until 10 September 2023.
Instructor(s):
Dr. Alexander Scheerer
Description:
Please find a detailed course description via the following link:
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Text Analytics (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Registration procedure:
This course has limited capacity. It is mandatory that you register via Portal2. You can register anytime during the official course registration period (August 15 – August 31, 2023). The time of your registration is not relevant as seats are not assigned on a first-come, first-served basis.
Please note that this lecture is accompanied by exercise classes, you can register for them via Portal 2.
Please note that this lecture is accompanied by exercise classes, you can register for them via Portal 2.
Instructor(s):
Dr. Jörg Schlötterer, Prof. Dr. Markus Strohmaier
Description:
Please find a detailed course description via the following link:
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
IT-Security (Seminar)
EN
Course type:
Seminar
ECTS:
4.0
Course suitable for:
Master
Language of instruction:
English
Instructor(s):
Prof. Dr. Frederik Armknecht
Semantic Web Technologies (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
The participants of this course learn about principles and applications of Semantic Web standards. They become familiar with their technical foundations such as representation and query languages, or logical inference. After taking this course, the students will be aware of the problems and benefits of semantic technologies in the context of tasks such as knowledge management, information search and data integration, and they will be capable of judging the applicability of these technologies for addressing practical challenges.
Methodological competence:
The participants learn how to design and implement Semantic Web applications. They are able to use standardized modeling languages for building knowledge representations, and to query these models by means of languages such as SPARQL.
Personal competence:
By jointly building a semantic web application, the students learn how to effectively work in teams. They improve upon their presentation skills by showing the outcomes of their projects to the other participants of the course.
The participants of this course learn about principles and applications of Semantic Web standards. They become familiar with their technical foundations such as representation and query languages, or logical inference. After taking this course, the students will be aware of the problems and benefits of semantic technologies in the context of tasks such as knowledge management, information search and data integration, and they will be capable of judging the applicability of these technologies for addressing practical challenges.
Methodological competence:
The participants learn how to design and implement Semantic Web applications. They are able to use standardized modeling languages for building knowledge representations, and to query these models by means of languages such as SPARQL.
Personal competence:
By jointly building a semantic web application, the students learn how to effectively work in teams. They improve upon their presentation skills by showing the outcomes of their projects to the other participants of the course.
Recommended requirement:
Examination achievement:
Regular exercises, team project, written examination (90 minutes)
Instructor(s):
Prof. Dr. Heiko Paulheim
Description:
- Vision and Principles of the Semantic Web
- Representation Languages (XML, RDF, RDF Schema, OWL)
- Knowledge Modeling: Ontologies and Linked Data
- Logical Reasoning in RDF and OWL
- Commercial and Open Source Tools and Systems
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Cryptographie II (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Learning target:
Fachkompetenz:
Die Studierenden können Mithilfe aktueller Techniken und Theorien der modernen Kryptographie die Sicherheit von kryptographischen Verfahren einschätzen bzw. Sicherheitsaussagen entsprechend zu beurteilen. Weiterhin sind sie in der Lage, Sicherheitsziele zu erkennen und entsprechende Techniken einzusetzen, die in Kryptographie I nicht behandelt werden konnten.
Methodenkompetenz:
Den Studierenden sind in der Lage, geeignete Methoden zu Sicherheitsanalyse von kryptographischen Verfahren auszuwählen und einzusetzen. Dazu gehören bspw. die Wahl der passenden Sicherheitsmodelle, das Beweisen der Sicherheit aufgrund klar präzisierter Annahmen und die Analyse gegebener Verfahren. Insbesondere besitzen die Studierenden die Fähigkeit, die Sicherheitsargumente für existierende Verfahren zu verstehen und einzuschätzen und auf neue zu übertragen. Weiterhin können sie Techniken und Protokolle einsetzen, um Sicherheitsziele zu erreichen, die mit den in Kryptographie I besprochenen Verfahren noch nicht möglich waren.
Personale Kompetenz:
Das analytische, konzentrierte und präzise Denken der Studierenden wird geschult. Durch die eigenständige Behandlung von Anwendungen, z.B. im Rahmen der Übungsaufgaben, wird ihr Abstraktionsvermögen weiterentwickelt und der Transfer des erlernten Stoffes auf verwandte Fragestellungen gefördert.
Die Studierenden können Mithilfe aktueller Techniken und Theorien der modernen Kryptographie die Sicherheit von kryptographischen Verfahren einschätzen bzw. Sicherheitsaussagen entsprechend zu beurteilen. Weiterhin sind sie in der Lage, Sicherheitsziele zu erkennen und entsprechende Techniken einzusetzen, die in Kryptographie I nicht behandelt werden konnten.
Methodenkompetenz:
Den Studierenden sind in der Lage, geeignete Methoden zu Sicherheitsanalyse von kryptographischen Verfahren auszuwählen und einzusetzen. Dazu gehören bspw. die Wahl der passenden Sicherheitsmodelle, das Beweisen der Sicherheit aufgrund klar präzisierter Annahmen und die Analyse gegebener Verfahren. Insbesondere besitzen die Studierenden die Fähigkeit, die Sicherheitsargumente für existierende Verfahren zu verstehen und einzuschätzen und auf neue zu übertragen. Weiterhin können sie Techniken und Protokolle einsetzen, um Sicherheitsziele zu erreichen, die mit den in Kryptographie I besprochenen Verfahren noch nicht möglich waren.
Personale Kompetenz:
Das analytische, konzentrierte und präzise Denken der Studierenden wird geschult. Durch die eigenständige Behandlung von Anwendungen, z.B. im Rahmen der Übungsaufgaben, wird ihr Abstraktionsvermögen weiterentwickelt und der Transfer des erlernten Stoffes auf verwandte Fragestellungen gefördert.
Recommended requirement:
Examination achievement:
Schriftliche Klausur (90 Minuten) oder mündliche Prüfung (30 Minuten)
Instructor(s):
Prof. Dr. Frederik Armknecht
Description:
In der Vorlesung erfolgt eine kurze Zusammenstellung der wichtigsten kryptographischen Grundalgorithmen und der für die Vorlesung relevanten mathematischen, algorithmischen und informations- und komplexitätstheoretischen Grundlagen. Diese werden einerseits vertieft und andererseits erweitert. Behandelte Themen sind beispielsweise
- moderne Techniken der Kryptanalyse und daraus ableitbare Designkriterien für kryptographische Verfahren
- kryptographische Protokolle
- Sicherheitsbeweise
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Model Driven Development (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will be familiar with the accepted best practices and technologies used in mainstream model-driven development as well as state-of-the-art modeling technologies emerging from research institutions.
Methodological competence:
Students will know how to apply modeling technologies in real-world projects.
Personal competence:
Students will have the capability to analyse, understand and model complex systems.
Students will be familiar with the accepted best practices and technologies used in mainstream model-driven development as well as state-of-the-art modeling technologies emerging from research institutions.
Methodological competence:
Students will know how to apply modeling technologies in real-world projects.
Personal competence:
Students will have the capability to analyse, understand and model complex systems.
Recommended requirement:
Examination achievement:
Written examination (90 minutes)
Instructor(s):
Prof. Dr. Colin Atkinson
Description:
The course focuses on the principles, practices and tools involved in advanced model-driven development. This includes established modelling standard languages (e. g. UML, ATL, OCL . . . ) and modelling infrastructures (e. g. MOF, EMF, etc. ) as well as leading edge, state-of-the-art modelling technologies (e. g. LML, PLM . . . ). Key topics addressed include –
- Multi-level modeling
- Meta-modeling
- Ontology engineering versus model engineering
- Model transformations
- Domain specific language definition and use
- Model creation and evolution best practices
- Model-driven software development
- Model checking and ontology validation
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Web Data Integration (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will be able to identify opportunities for employing Web data in business applications and will learn to select and apply appropriate techniques for integrating and cleansing Web data.
Methodological competence:
Students will be able to identify opportunities for employing Web data in business applications and will learn to select and apply appropriate techniques for integrating and cleansing Web data.
Methodological competence:
- Participants will acquire knowledge of the data integration process as well as the techniques that are used in each phase of the process.
- project organization skills
Personal competence:
- presentation skills
- team work skills.
Recommended requirement:
Examination achievement:
Written examination (90 minutes), project report, oral project presentation
Instructor(s):
Prof. Dr. Christian Bizer, Ralph Peeters, Alexander Brinkmann
Description:
The Web is developing from a medium for publishing textual documents into a medium for sharing structured data. In the course, students will learn how to integrate and cleanse data from this global data space for the later usage of the data within business applications. The course will cover the following topics:
- Heterogeneity and Distributedness
- The Data Integration Process
- Web Data Formats
- Schema Matching and Data Translation
- Identity Resolution
- Data Quality Assessment
- Data Fusion
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Business Informatics (Bachelor)
Algorithms and Data Structures (Lecture)
DE
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
6
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
Die Studierenden kennen effiziente Algorithmen und effektive Datenstrukturen für grundlegende Probleme der Informatik und können diese anwenden und in Computerprogramme umsetzen. Sie beherrschen weiterhin grundlegende Techniken des Entwurfs von Algorithmen und Datenstrukturen, sowie der Korrektheits- und Laufzeitanalyse von Algorithmen
Methodenkompetenz:
Die Studierenden können anwendungsrelevanten Berechnungsproblemen effiziente Algorithmen zuzuordnen bzw. diese entwickeln und
mittels dieser lösen.
Personale Kompetenz:
Die Studierenden können Berechnungsprobleme in Anwendungszusammenhängen identifizieren, sie formal spezifizieren und damit einer rechentechnischen Lösung zuführen. Sie können auf höherem Niveau abstrahieren und mit formalen Modellierungstechniken arbeiten.
Die Studierenden kennen effiziente Algorithmen und effektive Datenstrukturen für grundlegende Probleme der Informatik und können diese anwenden und in Computerprogramme umsetzen. Sie beherrschen weiterhin grundlegende Techniken des Entwurfs von Algorithmen und Datenstrukturen, sowie der Korrektheits- und Laufzeitanalyse von Algorithmen
Methodenkompetenz:
Die Studierenden können anwendungsrelevanten Berechnungsproblemen effiziente Algorithmen zuzuordnen bzw. diese entwickeln und
mittels dieser lösen.
Personale Kompetenz:
Die Studierenden können Berechnungsprobleme in Anwendungszusammenhängen identifizieren, sie formal spezifizieren und damit einer rechentechnischen Lösung zuführen. Sie können auf höherem Niveau abstrahieren und mit formalen Modellierungstechniken arbeiten.
Recommended requirement:
Examination achievement:
Schriftliche Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Matthias Krause, Linda Scheu-Hachtel
Description:
- Grundtechniken des Algorithmenentwurfs sowie der Laufzeitanalyse (Divide and Conquer, Greedyheuristiken, Dynamic Programming,…)
- Grundtechniken des Beweisens der Korrektheit von Algorithmen
- Sortieralgorithmen
- Hashing und hashingbasierte Algorithmen
- Advanced Data Structures
- Algorithmen für Suchbäume
- Graphalgorithmen (Tiefensuche, Breitensuche, Minimum Spanning Trees, Kürzeste-Wege-Algorithmen)
- Ausgewählte weitere Algorithmen (z.B. Pattern Matching, Automatenminimierung…)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
CS 560 Large-Scale Data Management (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire knowledge about methods and systems for managing large datasets and data-intensive computing.
Students will acquire knowledge about methods and systems for managing large datasets and data-intensive computing.
Methodological competence:
• Be able to judge, select, and use traditional or non-traditional data management systems for a given data management task
• Be able to solve computational problems involving large datasets
Personal competence:
• Study independently
• Presentation and writing skills
Recommended requirement:
Examination achievement:
Written examination, exercises
90 minutes
90 minutes
Instructor(s):
Prof. Dr. Rainer Gemulla
Description:
This course introduces the fundamental concepts and computational paradigms of large-scale data management and Big Data. This includes methods for storing, updating, querying, and analyzing large dataset as well as for data-intensive computing. The course covers concept, algorithms, and system issues; accompanying exercises provide hands-on experience. Topics include:
• Parallel and distributed databases
• MapReduce and its ecosystem
• NoSQL
• Stream processing
• Graph databases
• Parallel and distributed databases
• MapReduce and its ecosystem
• NoSQL
• Stream processing
• Graph databases
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Database Systems I (Lecture)
DE
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
Verständnis der Grundlagen der Datenmodellierung bzw. des Datenbankentwurfs und der Funktionsweise von relationalen Datenbankmanagementsystemen, insbesondere Anfragebearbeitung und Transaktionsverwaltung
Methodenkompetenz:
Abstraktion, Modellierung, Aufwandsabschätzung für Anfragen
Personale Kompetenz:
Verständnis der Rolle moderner Datenhaltung in einem Unternehmen
Verständnis der Grundlagen der Datenmodellierung bzw. des Datenbankentwurfs und der Funktionsweise von relationalen Datenbankmanagementsystemen, insbesondere Anfragebearbeitung und Transaktionsverwaltung
Methodenkompetenz:
Abstraktion, Modellierung, Aufwandsabschätzung für Anfragen
Personale Kompetenz:
Verständnis der Rolle moderner Datenhaltung in einem Unternehmen
Recommended requirement:
Examination achievement:
schriftliche Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Guido Moerkotte
Description:
Datenbankentwurf, Normalisierung, Anfragebearbeitung, Transaktionsverwaltung
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Database Systems I (Lecture)
DE
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
Verständnis der Grundlagen der Datenmodellierung bzw. des Datenbankentwurfs und der Funktionsweise von relationalen Datenbankmanagementsystemen, insbesondere Anfragebearbeitung und Transaktionsverwaltung
Methodenkompetenz:
Abstraktion, Modellierung, Aufwandsabschätzung für Anfragen
Personale Kompetenz:
Verständnis der Rolle moderner Datenhaltung in einem Unternehmen
Verständnis der Grundlagen der Datenmodellierung bzw. des Datenbankentwurfs und der Funktionsweise von relationalen Datenbankmanagementsystemen, insbesondere Anfragebearbeitung und Transaktionsverwaltung
Methodenkompetenz:
Abstraktion, Modellierung, Aufwandsabschätzung für Anfragen
Personale Kompetenz:
Verständnis der Rolle moderner Datenhaltung in einem Unternehmen
Recommended requirement:
Examination achievement:
schriftliche Klausur (90 Minuten)
Instructor(s):
Nazanin Rashedi
Description:
Datenbankentwurf, Normalisierung, Anfragebearbeitung, Transaktionsverwaltung
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
1
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Formal Foundations of Computer Science (Lecture)
DE
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
Die Studierenden beherrschen grundlegende für die Informatik rele-vanten Konzepte, Begriffsbildungen und wissenschaftlichen Arbeits-techniken aus Mathematik und Logik. Sie kennen weiterhin eine erste Auswahl an wichtigen Datenstrukturen und effizienten Algorithmen für grundlegende Probleme.
Methodenkompetenz:
Die Studierenden besitzen die Fähigkeit, informal gegebene Sachver-halte formal zu modellieren und die entstehenden formalen Struktu-ren bzgl. grundlegender Eigenschaften zu klassifizieren. Sie können weiterhin auf einem für Informatiker adäquaten Niveau gegebene Aussagen mathematisch beweisen.
Personale Kompetenz:
Die Studierenden besitzen ein Grundverständnis der för die Informa-tik wichtigen formalen Strukturen, Modelle und Arbeitstechniken. Sie können auf höherem Niveau abstrakt denken und formal modellieren.
Die Studierenden beherrschen grundlegende für die Informatik rele-vanten Konzepte, Begriffsbildungen und wissenschaftlichen Arbeits-techniken aus Mathematik und Logik. Sie kennen weiterhin eine erste Auswahl an wichtigen Datenstrukturen und effizienten Algorithmen für grundlegende Probleme.
Methodenkompetenz:
Die Studierenden besitzen die Fähigkeit, informal gegebene Sachver-halte formal zu modellieren und die entstehenden formalen Struktu-ren bzgl. grundlegender Eigenschaften zu klassifizieren. Sie können weiterhin auf einem für Informatiker adäquaten Niveau gegebene Aussagen mathematisch beweisen.
Personale Kompetenz:
Die Studierenden besitzen ein Grundverständnis der för die Informa-tik wichtigen formalen Strukturen, Modelle und Arbeitstechniken. Sie können auf höherem Niveau abstrakt denken und formal modellieren.
Recommended requirement:
Examination achievement:
Schriftliche Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Matthias Krause, Linda Scheu-Hachtel
Description:
- Grundlagen Aussagenlogik (Folgern, Beweisen)
- Mengen, Relationen, Abbildungen
- Grundlagen der Kombinatorik (Abzählen von endlichen Mengen, Abzählbarkeit)
- Einführung Graphentheorie
- Algebraische Strukturen (Halbgruppen, Gruppen, Homorphismen, Faktorstrukturen)
-
Grundlegende Berechnungsmodelle/
Endliche Automaten
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Integrated Information Systems (Wifo) (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Registration procedure:
This course does not have limited capacity. Registration via Portal 2 will be possible from August 15, 2023.
Please note that this lecture is accompanied by exercise classes, you can register for them via Portal 2.
Please note that this lecture is accompanied by exercise classes, you can register for them via Portal 2.
Learning target:
After attending the lecture, exercises and tutorials students are able to:
- model complex business processes based on popular modelling techniques
- discuss the requirements, characteristics and effects of integrated information systems in industrial companies, including complex process interdependencies
- complete basic tasks from different functional areas in a wide-spread integrated information system.
Recommended requirement:
Examination achievement:
Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Armin Heinzl, Dr. André Halckenhäußer, Tobias Maier
Description:
This course first outlines the basics of data and business process modelling based on wide-spread approaches such as entity relationship diagrams, event-driven process chains (EPC), and business process model and notation (BPMN). The remainder of the course then focuses on the use and purpose of integrated information systems across different functional areas in industrial companies. Finally, basics of management support systems such as business intelligence systems are addressed.
- Business Process Modelling
-
Application Systems in
- Research and Development
- Marketing and Sales
- Procurement and Warehousing
- Production
- Shipping and Customer Service
- Finance, Accounting, HR
- Planning and Control Systems
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Artificial Intelligence (Lecture)
DE
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
Ziele und Grundlagen der Künstlichen Intelligenz. Suchverfahren als universelle Problemlösungsverfahren. Problemkomplexität und Heuristische Lösungen. Eigenschaften und Zusammenhang zwischen unterschiedlichen Suchverfahren.
Methodenkompetenz:
Beschreibung konkreter Aufgaben als Such-, Constraint- oder Planungsproblem. Implementierung unterschiedlicher Suchverfahren und Heuristiken.
Ziele und Grundlagen der Künstlichen Intelligenz. Suchverfahren als universelle Problemlösungsverfahren. Problemkomplexität und Heuristische Lösungen. Eigenschaften und Zusammenhang zwischen unterschiedlichen Suchverfahren.
Methodenkompetenz:
Beschreibung konkreter Aufgaben als Such-, Constraint- oder Planungsproblem. Implementierung unterschiedlicher Suchverfahren und Heuristiken.
Recommended requirement:
Examination achievement:
Erfolgreiche Teilnahme am Übungsbetrieb
schriftliche Klausur (90 Minuten)
schriftliche Klausur (90 Minuten)
Instructor(s):
Dr. Christian Meilicke
Description:
- Problemeigenschaften und Problemtypen
- Problemlösen als Suche, Anwendung im Bereich Computerspiele
- Constraintprobleme und deren Lösung
- Logische Constraints
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAA 510 Introduction of partial differential equations (Lecture)
EN
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Attendance:
On-campus and online, live
Literature:
Script (online)
L.C. Evans: Partial Differential Equations
F. John: Partial Differential Equations
L.C. Evans: Partial Differential Equations
F. John: Partial Differential Equations
Examination achievement:
oral examination, 30 minutes
Instructor(s):
Dr. Ross Ogilvie, Prof. Dr. Martin Schmidt
Description:
- Basic notions of partial differential equations
- method of characteristics
- Laplace equations
- heat equations
- wave equation
MAB 401 Algebra (Lecture)
DE
Course type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
• Sicherer Umgang mit den algebraischen Grundstrukturen, Gruppen, Ringen, Körpern (BK1).
• Würdigung des Aufbaus dieser Grundstrukturen und wichtiger Beweise (BK1).
Methodenkompetenz:
• Gruppen als ordnendes Mittel für Symmetrien verstehen (BK1, BF2).
• Körpertheorie als modernes Werkzeug zur Lösung von mathematischen Fragen der Antike würdigen (BK1, BF2).
Personale Kompetenz:
• Strukturen und Symmetrien erkennen und präzisieren (BF1, BO2).
• Sicherer Umgang mit den algebraischen Grundstrukturen, Gruppen, Ringen, Körpern (BK1).
• Würdigung des Aufbaus dieser Grundstrukturen und wichtiger Beweise (BK1).
Methodenkompetenz:
• Gruppen als ordnendes Mittel für Symmetrien verstehen (BK1, BF2).
• Körpertheorie als modernes Werkzeug zur Lösung von mathematischen Fragen der Antike würdigen (BK1, BF2).
Personale Kompetenz:
• Strukturen und Symmetrien erkennen und präzisieren (BF1, BO2).
Recommended requirement:
Examination achievement:
Mündliche Prüfung oder schriftliche Klausur
Instructor(s):
Dr. Thomas Reichelt
Description:
• Gruppenbegriff, Eigenschaften und Anwendungen zyklischer und abelscher Gruppen, Beispiele, auflösbare Gruppen.
• Ringe, Ideale, Euklidische Ringe, Hauptidealringe, ZPW-Ringe, Quotientenringe.
• Körper, Körpererweiterungen, Galois-Theorie.
• Ringe, Ideale, Euklidische Ringe, Hauptidealringe, ZPW-Ringe, Quotientenringe.
• Körper, Körpererweiterungen, Galois-Theorie.
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAB 407 Number Theory (Lecture)
DE
Course type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Learning target:
Fachkompetenz:
• Grundbegriffe der elementaren Zahlentheorie (BF1, BK1)
• Algorithmische Verfahren (BK2, BO3)
• Zahlentheoretische Grundlagen der Kryptographie mit öffentlichen Schlüsseln sowie einiger kryptographischer Protokolle (BK3, BO3)
• Einfache Grundbegriffe der algebraischen Zahlentheorie für quadratische Zahlkörper (BF1, BK1)
• Deren Anwendung auf die Darstellung natürlicher Zahlen als Summen von Quadraten und die Berechnung (BF1, BK1)
Methodenkompetenz:
• Lösung einfacher linearer und quadratischer diophantischer Gleichungen (BF2, BK3)
• Bestimmung großer Primzahlen und Faktorisierung großer Zahlen (BF2, BK3, BO3)
• Approximation reeller Zahlen durch Kettenbrüche mit Anwendungen auf Kalenderberechnungen und Kryptologie (BF1, BF3, BO2)
• Anwendung des quadratischen Reziprozitätsgesetzes (BF1)
Personale Kompetenz:
• Algebraische Zahlentheorie und einige ihrer Anwendungen (BO2, BO3, BF2)
• Grundbegriffe der elementaren Zahlentheorie (BF1, BK1)
• Algorithmische Verfahren (BK2, BO3)
• Zahlentheoretische Grundlagen der Kryptographie mit öffentlichen Schlüsseln sowie einiger kryptographischer Protokolle (BK3, BO3)
• Einfache Grundbegriffe der algebraischen Zahlentheorie für quadratische Zahlkörper (BF1, BK1)
• Deren Anwendung auf die Darstellung natürlicher Zahlen als Summen von Quadraten und die Berechnung (BF1, BK1)
Methodenkompetenz:
• Lösung einfacher linearer und quadratischer diophantischer Gleichungen (BF2, BK3)
• Bestimmung großer Primzahlen und Faktorisierung großer Zahlen (BF2, BK3, BO3)
• Approximation reeller Zahlen durch Kettenbrüche mit Anwendungen auf Kalenderberechnungen und Kryptologie (BF1, BF3, BO2)
• Anwendung des quadratischen Reziprozitätsgesetzes (BF1)
Personale Kompetenz:
• Algebraische Zahlentheorie und einige ihrer Anwendungen (BO2, BO3, BF2)
Recommended requirement:
Examination achievement:
Mündliche Prüfung oder schriftliche Klausur
Instructor(s):
Dr. Thomas Reichelt
Description:
• Multiplikative Struktur der ganzen Zahlen, Modulorechnung mit Anwendungen in der Kryptographie
• Primzahlverteilung und Primzahltest
• Algorithmen zur Faktorisierung ganzer Zahlen
• Kettenbrüche und ihre Anwendungen
• Quadratische Zahlkörper, quadratische Formen und quadratische Reste, Berechnung der modularen Quadratwurzel
• Fermat-Vermutung für Zahlen und Polynome
• Primzahlverteilung und Primzahltest
• Algorithmen zur Faktorisierung ganzer Zahlen
• Kettenbrüche und ihre Anwendungen
• Quadratische Zahlkörper, quadratische Formen und quadratische Reste, Berechnung der modularen Quadratwurzel
• Fermat-Vermutung für Zahlen und Polynome
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAB 504 Mathematics and Information (Lecture)
EN
Course type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
4
Learning target:
Fachkompetenz:
Quantisierung von Information und inhaltliche Interpretation der entsprechenden Maße (MK1, MO2)
Verständnis für Möglichkeiten und Grenzen log-optimaler Anlagestrategien (MK2, MF1, MF2)
Verständnis für die Rolle der Linearen Algebra in der Informationssuche und der Klassifikation von Information (MK1, MK2, MF1, MF2)
Methodenkompetenz:
Umgang mit gängigen Informationsmaßen (MF2)
Datenkompression mit Huffman-Bäumen und mit Transformationen (MO2)
Berechnung log-optimaler und universeller Portfolios (MK2, MF1, MF2)
Berechnung von PageRank und verwandten Rängen (MK1, MK2, MF1, MF2)
Latente semantische Analyse via Singulärwertzerlegung (MK2, MF2)
Personale Kompetenz:
Fähigkeit, intuitiv gegebene Begriffe wie Information, optimale sichere Anlagestrategie, Wichtigkeit oder Ähnlichkeit von Dokumenten und Webseiten durch verschiedene Ansätze mathematisch zu modellieren und die Vor- und Nachteile der verschiedenen Möglichkeiten abzuschätzen (MK2, MF2, MO2, MO4)
Quantisierung von Information und inhaltliche Interpretation der entsprechenden Maße (MK1, MO2)
Verständnis für Möglichkeiten und Grenzen log-optimaler Anlagestrategien (MK2, MF1, MF2)
Verständnis für die Rolle der Linearen Algebra in der Informationssuche und der Klassifikation von Information (MK1, MK2, MF1, MF2)
Methodenkompetenz:
Umgang mit gängigen Informationsmaßen (MF2)
Datenkompression mit Huffman-Bäumen und mit Transformationen (MO2)
Berechnung log-optimaler und universeller Portfolios (MK2, MF1, MF2)
Berechnung von PageRank und verwandten Rängen (MK1, MK2, MF1, MF2)
Latente semantische Analyse via Singulärwertzerlegung (MK2, MF2)
Personale Kompetenz:
Fähigkeit, intuitiv gegebene Begriffe wie Information, optimale sichere Anlagestrategie, Wichtigkeit oder Ähnlichkeit von Dokumenten und Webseiten durch verschiedene Ansätze mathematisch zu modellieren und die Vor- und Nachteile der verschiedenen Möglichkeiten abzuschätzen (MK2, MF2, MO2, MO4)
Recommended requirement:
Examination achievement:
Mündliche Prüfung oder schriftliche Klausur
Instructor(s):
apl. Prof. Dr. Wolfgang Seiler
Description:
Shannons Entropie und abgeleitete Informationsmaße
Entropie und Datenkompression
Die Wettstrategie von Kelly
Log-optimale Portfolios
Universelle Portfolios
Vektorraummethoden in der Informationssuche
Matrixzerlegungen und latente semantische Analyse
PageRank und verwandte Verfahren
Entropie und Datenkompression
Die Wettstrategie von Kelly
Log-optimale Portfolios
Universelle Portfolios
Vektorraummethoden in der Informationssuche
Matrixzerlegungen und latente semantische Analyse
PageRank und verwandte Verfahren
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAC 404 Optimization (Lecture)
DE
Course type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
• Verständnis der wesentlichen Konzepte und Lösungsverfahren der Linearen Optimierung (BF1, BK1)
• Computerunterstütze Umsetzung anwendungsbezogener Fragestellungen (BK2, BK3, BO1)
• Querverbindungen zu anderen mathematischen Gebieten identifizierten Klassifikation und Interpretation numerischer Probleme (BK1, BO2)
Methodenkompetenz:
• Mathematische Modellierung eines Problems (BF3, BO3)
• Konkrete Problemlösungsstrategien und deren Interpretation (BF1, BF2)
Personale Kompetenz:
• Teamarbeit (BO1, BF4, BF5)
• Verständnis der wesentlichen Konzepte und Lösungsverfahren der Linearen Optimierung (BF1, BK1)
• Computerunterstütze Umsetzung anwendungsbezogener Fragestellungen (BK2, BK3, BO1)
• Querverbindungen zu anderen mathematischen Gebieten identifizierten Klassifikation und Interpretation numerischer Probleme (BK1, BO2)
Methodenkompetenz:
• Mathematische Modellierung eines Problems (BF3, BO3)
• Konkrete Problemlösungsstrategien und deren Interpretation (BF1, BF2)
Personale Kompetenz:
• Teamarbeit (BO1, BF4, BF5)
Recommended requirement:
Examination achievement:
Mündliche Prüfung oder schriftliche Klausur
Instructor(s):
Prof. Ph. D. Mathias Staudigl
Description:
• Lineare Programmierung: Simplex Verfahren, Dualität, innere Punkte Verfahren
• Graphentheorie: minimal spannende Bäume, kürzeste Wege, maximale Flüsse
• Ganzzahlige Programmierung: Branch and Bound Verfahren, Schnittebenenverfahren, Heuristiken
• Graphentheorie: minimal spannende Bäume, kürzeste Wege, maximale Flüsse
• Ganzzahlige Programmierung: Branch and Bound Verfahren, Schnittebenenverfahren, Heuristiken
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAC 403 Mathematical Finance (Lecture)
EN
Course type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Bachelor
Language of instruction:
English
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
• Grundbegriffe der Modellierung in der Finanzmathematik (BK2, BK4)
• Grundlagen der Martingaltheorie und des Itô-Kalküls (BK1, BK4)
• Bewertung und Absicherung riskanter Positionen in allgemeinen zeitdiskreten Marktmodellen, im Binomialmodell sowie in einfachen vollständigen Marktmodellen in stetiger Zeit wie etwa dem Bachelier oder dem Black-Scholes-Modell (BK1, BK2, BK3)
Methodenkompetenz:
• Grundprinzipien des dynamischen Risikomanagement (BF2, BF3, BO1, BO3)
• Beherrschung der Terminologie der Finanzmathematik wie z.B. den “Greeks” (BF4, BF5, BO1)
• Erkennen, in welchen Situationen welche Bewertungsmethoden für Risiken sinnvoll sein können (BF2, BF3, BF4, BF5)
Personale Kompetenz:
• Teamarbeit (BF4)
• Grundbegriffe der Modellierung in der Finanzmathematik (BK2, BK4)
• Grundlagen der Martingaltheorie und des Itô-Kalküls (BK1, BK4)
• Bewertung und Absicherung riskanter Positionen in allgemeinen zeitdiskreten Marktmodellen, im Binomialmodell sowie in einfachen vollständigen Marktmodellen in stetiger Zeit wie etwa dem Bachelier oder dem Black-Scholes-Modell (BK1, BK2, BK3)
Methodenkompetenz:
• Grundprinzipien des dynamischen Risikomanagement (BF2, BF3, BO1, BO3)
• Beherrschung der Terminologie der Finanzmathematik wie z.B. den “Greeks” (BF4, BF5, BO1)
• Erkennen, in welchen Situationen welche Bewertungsmethoden für Risiken sinnvoll sein können (BF2, BF3, BF4, BF5)
Personale Kompetenz:
• Teamarbeit (BF4)
Recommended requirement:
Examination achievement:
Je nach Teilnehmerzahl schriftliche Klausur oder mündliche Prüfung (wird zu Beginn der Vorlesung bekannt gegeben)
Prüfungsvorleistung: erfolgreiche Teilnahme an den Übungen
Prüfungsvorleistung: erfolgreiche Teilnahme an den Übungen
Instructor(s):
Prof. Dr. Simon Weißmann
Description:
• Mathematische Grundlagen der zeitlich diskreten Finanzmathematik wie bedingte Erwartungen, Martingale und elementare Funktionalanalysis
• Modellierung von Finanzmärkten in diskreter Zeit
• Arbitragetheorie in diskreter Zeit; insb. Fundamentalsatz der arbitragefreien Bewertung (FTAP), sowie Bewertung und Absicherung von europäischen und Optionen in vollständigen und unvollständigen Marktmodellen
• Binomialmodell von Cox, Ross und Rubinstein
• Amerikanische Optionen und optimales Stoppen in diskreter Zeit
• Mathematische Grundlagen der Finanzmathematik in stetiger Zeit wie Stieltjes-Integration, pfadweiser Itô-Kalkül, elementare partielle Differentialgleichungen
• Modellierung von Finanzmärkten in stetiger Zeit
• Absicherung von Optionen im Bachelier-Modell
• Black-Scholes-Formel
• Variance-swaps, VIX, CPPI
• Modellierung von Finanzmärkten in diskreter Zeit
• Arbitragetheorie in diskreter Zeit; insb. Fundamentalsatz der arbitragefreien Bewertung (FTAP), sowie Bewertung und Absicherung von europäischen und Optionen in vollständigen und unvollständigen Marktmodellen
• Binomialmodell von Cox, Ross und Rubinstein
• Amerikanische Optionen und optimales Stoppen in diskreter Zeit
• Mathematische Grundlagen der Finanzmathematik in stetiger Zeit wie Stieltjes-Integration, pfadweiser Itô-Kalkül, elementare partielle Differentialgleichungen
• Modellierung von Finanzmärkten in stetiger Zeit
• Absicherung von Optionen im Bachelier-Modell
• Black-Scholes-Formel
• Variance-swaps, VIX, CPPI
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Markovketten (Lecture)
DE
Course type:
Lecture
ECTS:
5.0
Course suitable for:
Bachelor
Language of instruction:
German
Attendance:
Online, recorded
Instructor(s):
Prof. Dr. Leif Döring, Prof. Dr. Martin Slowik
MAT 301 Analysis I (Lecture)
DE
Course type:
Lecture
ECTS:
10.0 (Modul/e)
Course suitable for:
Bachelor, Master
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
• Grundbegriffe der reellen Analysis (BF1, BK1)
• Konvergenz von Folgen und Reihen (BK1)
• Stetigkeit von Funktionen in einer Variablen (BK1)
• Differenzierbarkeit von Funktionen in einer Variablen (BK1)
• Riemanintegral von Funktionen in einer Variablen (BK1)
Methodenkompetenz:
• mathematische Beweisführung (BF1, BO2)
• Hantieren mit Gleichungen und Ungleichungen (BF1, BO2)
• Berechnen von Grenzwerten (BF1,BO3)
• Kurvendiskussion (BF2, BO3)
• Berechnen von unbestimmten und bestimmten Integralen (BO2,BO3)
Personale Kompetenz:
• Teamarbeit (BF4)
• Grundbegriffe der reellen Analysis (BF1, BK1)
• Konvergenz von Folgen und Reihen (BK1)
• Stetigkeit von Funktionen in einer Variablen (BK1)
• Differenzierbarkeit von Funktionen in einer Variablen (BK1)
• Riemanintegral von Funktionen in einer Variablen (BK1)
Methodenkompetenz:
• mathematische Beweisführung (BF1, BO2)
• Hantieren mit Gleichungen und Ungleichungen (BF1, BO2)
• Berechnen von Grenzwerten (BF1,BO3)
• Kurvendiskussion (BF2, BO3)
• Berechnen von unbestimmten und bestimmten Integralen (BO2,BO3)
Personale Kompetenz:
• Teamarbeit (BF4)
Recommended requirement:
Examination achievement:
schriftliche Klausur
Instructor(s):
Prof. Dr. Martin Schmidt, Nicolas Hasse
Description:
• Mengen und Abbildungen
• reelle Zahlen
• Zahlenfolgen und Reihen
• Funktionen in einer reellen Variablen
• reelle Zahlen
• Zahlenfolgen und Reihen
• Funktionen in einer reellen Variablen
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAT 303 Linear Algebra I (Lecture)
DE
Course type:
Lecture
ECTS:
9.0 (Modul/e)
Course suitable for:
Bachelor, Master
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
• Kenntnis der wesentlichen Ideen und Methoden der Linearen Algebra, Kenntnis der wesentlichen mathematischen Beweismethoden (BK1).
Methodenkompetenz:
• Grundstrukturen der Linearen Algebra als Grundstrukturen der Mathematik würdigen und sicher mit ihnen umgehen (BK1).
• Lineare Gleichungssysteme in Anwendungen erkennen und professionell lösen (BF2).
Personale Kompetenz:
• Strukturiertes Denken (BO2).
• Teamarbeit (BF4).
• Kommunikationsfähigkeit (BO1).
• Kenntnis der wesentlichen Ideen und Methoden der Linearen Algebra, Kenntnis der wesentlichen mathematischen Beweismethoden (BK1).
Methodenkompetenz:
• Grundstrukturen der Linearen Algebra als Grundstrukturen der Mathematik würdigen und sicher mit ihnen umgehen (BK1).
• Lineare Gleichungssysteme in Anwendungen erkennen und professionell lösen (BF2).
Personale Kompetenz:
• Strukturiertes Denken (BO2).
• Teamarbeit (BF4).
• Kommunikationsfähigkeit (BO1).
Recommended requirement:
Examination achievement:
schriftliche Klausur
Instructor(s):
Prof. Dr. Claus Hertling
Description:
• Gruppen, Ringe, Körper, Vektorräume, Lineare Abbildungen, Matrizen, Lineare Gleichungssysteme, Determinanten, Eigenwerte und Diagonalisierung, Euklidische Vektorräume.
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAT 306 Numerical Mathematics (Lecture)
DE
Course type:
Lecture
ECTS:
9.0
Course suitable for:
Bachelor, Master
Language of instruction:
German
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Fachkompetenz:
• Verständnis der Grundbegriffe und grundlegenden Methoden der Numerischen Mathematik (BF1, BK1)
• Algorithmisches Denken und Implementierung grundlegender Verfahren zur Bestimmung von Näherungslösungen (BK3)
• Klassifikation und Interpretation numerischer Probleme (BK1, BO3)
Methodenkompetenz:
• Mathematische Modellierung eines (Anwendungs-)Problems (BF3, BO3)
• Konkrete Problemlösungsstrategien und deren Interpretation (BF1, BF2)
Personale Kompetenz:
• Teamarbeit (BO1,BF4)
• Verständnis der Grundbegriffe und grundlegenden Methoden der Numerischen Mathematik (BF1, BK1)
• Algorithmisches Denken und Implementierung grundlegender Verfahren zur Bestimmung von Näherungslösungen (BK3)
• Klassifikation und Interpretation numerischer Probleme (BK1, BO3)
Methodenkompetenz:
• Mathematische Modellierung eines (Anwendungs-)Problems (BF3, BO3)
• Konkrete Problemlösungsstrategien und deren Interpretation (BF1, BF2)
Personale Kompetenz:
• Teamarbeit (BO1,BF4)
Recommended requirement:
Examination achievement:
schriftliche Klausur
Instructor(s):
Prof. Dr. Simone Göttlich
Description:
• Numerik linearer Gleichungssysteme
• Störungstheorie und Fehleranalyse
• Lineare Ausgleichsrechnung
• Eigenwertprobleme
• Nichtlineare Gleichungssysteme: Fixpunktiterationen, insbesondere Newton-Verfahren
• Interpolation und Splines
• Numerische Integration
• Störungstheorie und Fehleranalyse
• Lineare Ausgleichsrechnung
• Eigenwertprobleme
• Nichtlineare Gleichungssysteme: Fixpunktiterationen, insbesondere Newton-Verfahren
• Interpolation und Splines
• Numerische Integration
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Mathematische Methoden der Big Data Analytics 1 (Lecture)
DE
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor
Language of instruction:
German
Instructor(s):
Prof. Dr. Martin Schlather
Practical Computer Science I (Lecture)
DE
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor, Master
Language of instruction:
German
Credit hours 1:
6
Learning target:
Fachkompetenz:
Die Studierenden können selbständig Algorithmen zu vorgegebenen Problemen entwerfen und in Java, das im parallel laufenden Pro-grammierkurs I unterrichtet wird, objektorientiert programmieren. Methodenkompetenz:
Algorithmenentwurf, Bewertung von vorgegeben Algorithmen Personale Kompetenz:
Kreativität beim Entwurf von Algorithmen, Teamfähigkeit
Die Studierenden können selbständig Algorithmen zu vorgegebenen Problemen entwerfen und in Java, das im parallel laufenden Pro-grammierkurs I unterrichtet wird, objektorientiert programmieren. Methodenkompetenz:
Algorithmenentwurf, Bewertung von vorgegeben Algorithmen Personale Kompetenz:
Kreativität beim Entwurf von Algorithmen, Teamfähigkeit
Recommended requirement:
Examination achievement:
Studienbeginn ab HWS 2011:
Erfolgreiche Teilnahme am Übungsbetrieb
schriftliche Klausur (90 Minuten)
Erfolgreiche Teilnahme am Übungsbetrieb
schriftliche Klausur (90 Minuten)
Studienbeginn vor HWS 2011:
schriftliche Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Frederik Armknecht, Jochen Schäfer
Description:
Vom Problem zum Algorithmus, vom Algorithmus zum Programm
- Entwurf von Algorithmen: schrittweise Verfeinerung, Modularität, Objektorientierung (Klassenhierarchien, Vererbung), Rekursion
- Die objektorientierte Programmiersprache Java
- Einfache Datenstrukturen (verkettete Liste, Binärbaum, B-Baum)
- Modellierung mit UML: Klassendiagramme, Aktivitätsdiagramme, Zustandsdiagramme
- Einführung in die Theorie der Algorithmen: Berechenbarkeit, Komplexität (O-Kalkül), Testen und Verifikation von Algorithmen und Programmen
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Programming Lab I (Lecture)
DE
Course type:
Lecture
ECTS:
5.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
On-campus and online, live
Learning target:
Fachkompetenz:
- Gründliche Kenntnis der Basiskonzepte der Programmiersprache Java
- Verständnis des Konzepts der Objektorientierung
- Kenntnisse der algorithmischen Prinzipien Iteration und Rekursion
- Basiswissen über das Arbeiten unter einem Linux-Betriebssystem
Methodenkompetenz:
- Fähigkeit, Algorithmen zu entwerfen
- Fähigkeit, komplexe Algorithmen in Java ohne Einsatz importierter Methoden zu programmieren
- Fähigkeit, rekursiv zu programmieren
Personale Kompetenz:
- Eigenverantwortliches Arbeiten
- Teamfähigkeit
Recommended requirement:
Examination achievement:
Programmiertestate, Programmierprojekte, Programming Competence Test (180 Minuten)
Instructor(s):
Dr. Ursula Rost
Description:
Im Programmierpraktikum I werden grundlegende Kenntnisse der objektorientierten Programmierung auf Basis der Sprache Java vermittelt.
Die Studierenden werden von dieser Sprache vor allem folgende Grundmerkmale und Konzepte kennenlernen:
Die Programmierausbildung erfolgt auf der Basis des Betriebssystems Linux. Hierzu werden ebenfalls Grundkenntnisse vermittelt, die es ermöglichen, einfache Java-Programme zu entwickeln. Im Laufe des Kurses wird darüber hinaus eine einfache Entwicklungsumgebung eingeführt.
Die Studierenden werden von dieser Sprache vor allem folgende Grundmerkmale und Konzepte kennenlernen:
- Basiskonzepte der Programmierung: einfache Datentypen, Variablen, Operatoren, Anweisungen, Kontrollstrukturen
- Zusammengesetzte Datentypen (Felder)
- Das Konzept der objektorientierten Programmierung
- Klassen (Attribute, Methoden, Konstruktoren)
- Vererbung
- Pakete, abstrakte Klassen und Interfaces
- Java API und wichtige Hilfsklassen
- Ausnahmebehandlung: Exceptions
- Programmierung Grafischer Oberflächen mit Swing
Die Programmierausbildung erfolgt auf der Basis des Betriebssystems Linux. Hierzu werden ebenfalls Grundkenntnisse vermittelt, die es ermöglichen, einfache Java-Programme zu entwickeln. Im Laufe des Kurses wird darüber hinaus eine einfache Entwicklungsumgebung eingeführt.
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Programming Course (Lecture w/
EN
Course type:
Lecture w/
ECTS:
Course suitable for:
Bachelor
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Dr. Ursula Rost
Stochastik 1
Klausur (Lecture)
DE
Course type:
Lecture
ECTS:
9.0
Course suitable for:
Bachelor
Language of instruction:
German
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Leif Döring
Stochastik 1
Repetitorium (Lecture)
DE
Course type:
Lecture
ECTS:
9.0
Course suitable for:
Bachelor
Language of instruction:
German
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Leif Döring
Stochastik 1 (Lecture)
DE
Course type:
Lecture
ECTS:
9.0
Course suitable for:
Bachelor
Language of instruction:
German
Examination achievement:
schriftliche Präsenzprüfung
Instructor(s):
Prof. Dr. Leif Döring
Business Informatics I: Introduction and Foundations (Lecture)
DE
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
2
Attendance:
Live & on-campus
Registration procedure:
This course does not have limited capacity. Registration via Portal 2 will be possible from August 15, 2023.
Please note that this lecture is accompanied by exercise classes and tutorials, you can register for them via Portal 2.
Please note that this lecture is accompanied by exercise classes and tutorials, you can register for them via Portal 2.
Learning target:
Anhand der Vorlesung sollen Sie erlernen, dass Wirtschaftsinformatik mehr als die Nutzung von Informationstechnik ist. Die Inhalte werden Sie im weiteren Verlauf Ihres Studiums sowie bei der Verwertung des erlernten Wissens in ihrer Bachelorarbeit nutzenbringend verwerten können.
Recommended requirement:
Examination achievement:
80% Schriftliche Klausur (90 Minuten)
20% Gruppenarbeit
20% Gruppenarbeit
Instructor(s):
Prof. Dr. Armin Heinzl, Luis Oberste
Description:
Die Vorlesung Wirtschaftsinformatik I vermittelt die Fundamente der Wirtschaftsinformatik als wissenschaftliche Disziplin. Im Rahmen einer Einführung werden unter anderem der Gegenstand, der Wissenschaftscharakter, die Forschungsziele, -theorien, und -methoden sowie Nachbardisziplinen und ein Ländervergleich behandelt. Im Rahmen der Grundlegung werden zentrale Inhalte wie Informationsbedarf, Informationsverhalten, Informationssystem, Informationsinfrastruktur, Benutzerverhalten, Aspekte einer Entwurfslehre und Inhalte der Evaluationsforschung vermittelt.
Bitte klicken Sie hier für weitere Informationen.
Bitte klicken Sie hier für weitere Informationen.
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Business Informatics III: Development and Management of Information Systems (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Registration procedure:
This course does not have limited capacity. Registration via Portal 2 will be possible from August 15, 2023.
Learning target:
In order to be able to deal with these challenges, the “Development and Management of Information Systems” course is designed to introduce students to the various stages of the life cycle of an IS. Starting with the initial idea and conception of a system, the course will cover the process from development to introduction and, finally, application and value creation. In doing so, students will get to know the various entities and roles involved in IS development and management.
The primary objective of the course is to enable students to play a vital role at the intersection of technical and business issue, being able to bridge the gap between a company’s end users and IT experts. In doing so, they shall understand that IS transcend mere technological artifacts but constitute complex socio-technical phenomena.
To support students in their learning, the course will offer a basic introduction to the IS phenomenon, system types, and roles involved in development, introduction, management, and use of IS. Subsequently, each of these phases will be looked at in greater detail. For each phase, both the processes as well as at the contents of each domain will be introduced and discussed. Beyond the presentation of basic concepts, methods, and theories, the course will also provide students with opportunities to extend and practice their theoretical knowledge with interactive elements, an industry speaker, and a case study.
The primary objective of the course is to enable students to play a vital role at the intersection of technical and business issue, being able to bridge the gap between a company’s end users and IT experts. In doing so, they shall understand that IS transcend mere technological artifacts but constitute complex socio-technical phenomena.
To support students in their learning, the course will offer a basic introduction to the IS phenomenon, system types, and roles involved in development, introduction, management, and use of IS. Subsequently, each of these phases will be looked at in greater detail. For each phase, both the processes as well as at the contents of each domain will be introduced and discussed. Beyond the presentation of basic concepts, methods, and theories, the course will also provide students with opportunities to extend and practice their theoretical knowledge with interactive elements, an industry speaker, and a case study.
Recommended requirement:
Examination achievement:
Written exam (30%) (90 Minuten)
Case study write-up (70%)
Case study write-up (70%)
Instructor(s):
Prof. Dr. Hartmut Höhle, Jan Schilpp
Description:
During the last decades we witnessed a growing importance of Information Systems (IS) in the business world along with faster and faster innovation cycles. A case in point is the growing IS-related expenditure of corporations, forecasted to total EUR 2.63 trillion in 2012 – a 4.7% growth over 2011 (Gartner 2013). Ranging from the enrichment of routine working tasks (i.e., employee portals to integrate disparate applications, data, and processes (Daniel and White 2005)) to the e-enabled integration of entire business eco-systems (e.g., platform-based integration of supply chains (e.g., Kroenke 2010)), IS have become a vital backbone of businesses.
Consequently, the ability to use IS in a way supporting the overall value proposition of a corporation has become a central success determinant for many firms. Accordingly, the “Development and Management of Information Systems” course is designed to introduce students to the nature, role, and potentials of IS in corporations and enable them to serve as a meaningful interface between technology and business.
Once filling this role in a business context, the future IS professionals are likely to be facing two major trends: the increasing industrialization of IS (Brenner et al. 2007; Daberkow and Radtke 2008; Walter et al. 2007) and a shift towards service-orientation in IT organizations and processes (Hochstein et al. 2005; Roewekamp 2007). This brings about challenges such as, among others, managing the trade-off between efficient execution and effective offering or recognizing and mitigating conflicting expectations and goals among the many entities (i.e., software producers, consultants, corporate users, customers) and roles (i.e., business professionals, technical staff, corporate management) involved in an IS.
Consequently, the ability to use IS in a way supporting the overall value proposition of a corporation has become a central success determinant for many firms. Accordingly, the “Development and Management of Information Systems” course is designed to introduce students to the nature, role, and potentials of IS in corporations and enable them to serve as a meaningful interface between technology and business.
Once filling this role in a business context, the future IS professionals are likely to be facing two major trends: the increasing industrialization of IS (Brenner et al. 2007; Daberkow and Radtke 2008; Walter et al. 2007) and a shift towards service-orientation in IT organizations and processes (Hochstein et al. 2005; Roewekamp 2007). This brings about challenges such as, among others, managing the trade-off between efficient execution and effective offering or recognizing and mitigating conflicting expectations and goals among the many entities (i.e., software producers, consultants, corporate users, customers) and roles (i.e., business professionals, technical staff, corporate management) involved in an IS.
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Business Informatics (Master)
Advanced Methods in Text Analytics (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Simone Paolo Ponzetto, Dr. Daniel Ruffinelli
Advanced Software Engineering (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
After taking the course, students will be familiar with the latest state-of-the-art techniques for specifying the externally visible properties of a software system/
Participants will know how to use the expertise acquired during the course to describe the requirements that a system/
With the acquired skills and know-how, students will be able to play a key role in projects involving the development of systems, components and software applications.
After taking the course, students will be familiar with the latest state-of-the-art techniques for specifying the externally visible properties of a software system/
Participants will know how to use the expertise acquired during the course to describe the requirements that a system/
With the acquired skills and know-how, students will be able to play a key role in projects involving the development of systems, components and software applications.
Recommended requirement:
Examination achievement:
Written examination, 90 minutes
Instructor(s):
Prof. Dr. Colin Atkinson
Description:
The course deals with the model-based specification of software systems and components as well as their verification, validation and quality assurance. The emphasis is on view-based specification methods that use multiple views, expressed in multiple languages, to describe orthogonal aspects of software systems/
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAB 502 Algebraic Number Theory (Lecture)
DE
Course type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
German
Credit hours 1:
4
Attendance:
Online, recorded
Learning target:
Fachkompetenz:
Solides Verständnis für grundlegende Fragen der algebraischen Zahlentheorie
Methodenkompetenz:
Fähigkeit, abstrakte algebraische Techniken in einem konkreten komplexen mathematischen Kontext anzuwenden.
Personale Kompetenz:
Fähigkeit, in abstrakten Strukturen zu denken; Ahnung von heutiger Forschung (Stichwort: „Langlandsprogramm“)
Solides Verständnis für grundlegende Fragen der algebraischen Zahlentheorie
Methodenkompetenz:
Fähigkeit, abstrakte algebraische Techniken in einem konkreten komplexen mathematischen Kontext anzuwenden.
Personale Kompetenz:
Fähigkeit, in abstrakten Strukturen zu denken; Ahnung von heutiger Forschung (Stichwort: „Langlandsprogramm“)
Recommended requirement:
Examination achievement:
Mündliche Prüfung oder schriftliche Klausur
Instructor(s):
Dr. Thomas Reichelt
Description:
Begriff der Ganzheit, Dedekindringe, Ringerweiterungen, Klassenzahl, Dirichletscher Einheitensatz, Verzweigungstheorie,
Bewertungen, Lokalisierungen, Adelisierungen, Kreisteilungskörper als Spezialfall, Ausblick auf Zetafunktionen
Bewertungen, Lokalisierungen, Adelisierungen, Kreisteilungskörper als Spezialfall, Ausblick auf Zetafunktionen
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Blockchain Security (Lecture)
DE
Course type:
Lecture
ECTS:
Course suitable for:
Master
Language of instruction:
German
Instructor(s):
Prof. Dr. Frederik Armknecht, Jochen Schäfer
Compiler Construction (Lecture w/
EN
Course type:
Lecture w/
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
4
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr.-Ing. Roland Leißa
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Copulas und Konkordanzmaße (Lecture)
DE
Course type:
Lecture
ECTS:
4.0
Course suitable for:
Master
Language of instruction:
German
Instructor(s):
Prof. Dr. Klaus D. Schmidt
CS 560 Large-Scale Data Management (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
4
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire knowledge about methods and systems for managing large datasets and data-intensive computing.
Students will acquire knowledge about methods and systems for managing large datasets and data-intensive computing.
Methodological competence:
• Be able to judge, select, and use traditional or non-traditional data management systems for a given data management task
• Be able to solve computational problems involving large datasets
Personal competence:
• Study independently
• Presentation and writing skills
Recommended requirement:
Examination achievement:
Written examination, exercises
90 minutes
90 minutes
Instructor(s):
Prof. Dr. Rainer Gemulla
Description:
This course introduces the fundamental concepts and computational paradigms of large-scale data management and Big Data. This includes methods for storing, updating, querying, and analyzing large dataset as well as for data-intensive computing. The course covers concept, algorithms, and system issues; accompanying exercises provide hands-on experience. Topics include:
• Parallel and distributed databases
• MapReduce and its ecosystem
• NoSQL
• Stream processing
• Graph databases
• Parallel and distributed databases
• MapReduce and its ecosystem
• NoSQL
• Stream processing
• Graph databases
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Data Mining (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Registration procedure:
Please note that there is no second date for the exam.
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of data mining. Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of data mining. Methodological competence:
- Successful participants will be able to identify opportunities for applying data mining in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project organisation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), project report, oral project presentation
Instructor(s):
Prof. Dr. Heiko Paulheim
Description:
The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:
- Goals and Principles of Data Mining
- Data Representation and Preprocessing
- Clustering
- Classification
- Association Analysis
- Text Mining
- Systems and Applications (e. g. Retail, Finance, Web Analysis)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
1
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Decision Support (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
Students will acquire basic knowledge of the techniques, opportunities and applications of decision theory.
Methodological competence:
- Successful participants will be able to identify opportunities for decision support in an enterprise environment, select and apply appropriate techniques, and interpret the results.
- project presentation skills
Personal competence:
- team work skills
- presentation skills
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments, case studies
Instructor(s):
Lea Cohausz, Prof. Dr. Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
- Decision Theory
- Decision- and Business Rules
- Planning Methods and Algorithms
- Probabilistic Graphical Models
- Game Theory and Mechanism Design
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Higher Level Computer Vision – Exercise (Lecture w/
EN
Course type:
Lecture w/
ECTS:
6.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr.-Ing. Margret Keuper
Higher Level Computer Vision – Lecture (Lecture w/
EN
Course type:
Lecture w/
ECTS:
6.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr.-Ing. Margret Keuper
IE 675b Machine Learning (Lecture)
EN
Course type:
Lecture
ECTS:
9.0
Course suitable for:
Master
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Rainer Gemulla
IS 613 Applied Project in Design Thinking and Lean Software Development (Lecture w/
EN
Course type:
Lecture w/
ECTS:
6.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
On-campus and online, live
Registration procedure:
This course has limited capacity. Applications are only possible by applying over our chair's application tool (accessible inside the university network or per VPN only). If you are applying for IS 613 and IS 615, one application is sufficient but indicate that you're applying for both courses. Only within the period listed below is an application possible!
Application period: 01.08.23 – 01.09.23
Requirements:
- Short cover letter (1–2 paragraphs) about your motivation and your previous experience in software development
- Curriculum vitae and study results (transcript of records)
Confirmations will be sent on Monday, 04 September 2023. Cancellation is possible until 10 September 2023.
Instructor(s):
Dr. Alexander Scheerer, Deborah Mateja
Description:
Please find a detailed course description via the following link:
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
IS 614 Corporate Knowledge Management (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Registration procedure:
This course does not have limited capacity. Registration via Portal 2 will be possible from August 15, 2023.
Instructor(s):
Deborah Mateja
Description:
Please find a detailed course description via the following link:
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
IS 615 Design Thinking and Lean Development in Enterprise Software Development (Lecture)
EN
Course type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
On-campus and online, live
Registration procedure:
This course has limited capacity. Applications are only possible by applying over our chair's application tool (accessible inside the university network or per VPN only). If you are applying for IS 613 and IS 615, one application is sufficient but indicate that you're applying for both courses. Only within the period listed below is an application possible!
Application period: 01.08.23 – 01.09.23
Requirements:
- Short cover letter (1–2 paragraphs) about your motivation and your previous experience in software development
- Curriculum vitae and study results (transcript of records)
Confirmations will be sent on Monday, 04 September 2023. Cancellation is possible until 10 September 2023.
Instructor(s):
Dr. Alexander Scheerer
Description:
Please find a detailed course description via the following link:
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Text Analytics (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Registration procedure:
This course has limited capacity. It is mandatory that you register via Portal2. You can register anytime during the official course registration period (August 15 – August 31, 2023). The time of your registration is not relevant as seats are not assigned on a first-come, first-served basis.
Please note that this lecture is accompanied by exercise classes, you can register for them via Portal 2.
Please note that this lecture is accompanied by exercise classes, you can register for them via Portal 2.
Instructor(s):
Dr. Jörg Schlötterer, Prof. Dr. Markus Strohmaier
Description:
Please find a detailed course description via the following link:
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
IT-Security (Seminar)
EN
Course type:
Seminar
ECTS:
4.0
Course suitable for:
Master
Language of instruction:
English
Instructor(s):
Prof. Dr. Frederik Armknecht
Semantic Web Technologies (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
The participants of this course learn about principles and applications of Semantic Web standards. They become familiar with their technical foundations such as representation and query languages, or logical inference. After taking this course, the students will be aware of the problems and benefits of semantic technologies in the context of tasks such as knowledge management, information search and data integration, and they will be capable of judging the applicability of these technologies for addressing practical challenges.
Methodological competence:
The participants learn how to design and implement Semantic Web applications. They are able to use standardized modeling languages for building knowledge representations, and to query these models by means of languages such as SPARQL.
Personal competence:
By jointly building a semantic web application, the students learn how to effectively work in teams. They improve upon their presentation skills by showing the outcomes of their projects to the other participants of the course.
The participants of this course learn about principles and applications of Semantic Web standards. They become familiar with their technical foundations such as representation and query languages, or logical inference. After taking this course, the students will be aware of the problems and benefits of semantic technologies in the context of tasks such as knowledge management, information search and data integration, and they will be capable of judging the applicability of these technologies for addressing practical challenges.
Methodological competence:
The participants learn how to design and implement Semantic Web applications. They are able to use standardized modeling languages for building knowledge representations, and to query these models by means of languages such as SPARQL.
Personal competence:
By jointly building a semantic web application, the students learn how to effectively work in teams. They improve upon their presentation skills by showing the outcomes of their projects to the other participants of the course.
Recommended requirement:
Examination achievement:
Regular exercises, team project, written examination (90 minutes)
Instructor(s):
Prof. Dr. Heiko Paulheim
Description:
- Vision and Principles of the Semantic Web
- Representation Languages (XML, RDF, RDF Schema, OWL)
- Knowledge Modeling: Ontologies and Linked Data
- Logical Reasoning in RDF and OWL
- Commercial and Open Source Tools and Systems
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Cryptographie II (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Learning target:
Fachkompetenz:
Die Studierenden können Mithilfe aktueller Techniken und Theorien der modernen Kryptographie die Sicherheit von kryptographischen Verfahren einschätzen bzw. Sicherheitsaussagen entsprechend zu beurteilen. Weiterhin sind sie in der Lage, Sicherheitsziele zu erkennen und entsprechende Techniken einzusetzen, die in Kryptographie I nicht behandelt werden konnten.
Methodenkompetenz:
Den Studierenden sind in der Lage, geeignete Methoden zu Sicherheitsanalyse von kryptographischen Verfahren auszuwählen und einzusetzen. Dazu gehören bspw. die Wahl der passenden Sicherheitsmodelle, das Beweisen der Sicherheit aufgrund klar präzisierter Annahmen und die Analyse gegebener Verfahren. Insbesondere besitzen die Studierenden die Fähigkeit, die Sicherheitsargumente für existierende Verfahren zu verstehen und einzuschätzen und auf neue zu übertragen. Weiterhin können sie Techniken und Protokolle einsetzen, um Sicherheitsziele zu erreichen, die mit den in Kryptographie I besprochenen Verfahren noch nicht möglich waren.
Personale Kompetenz:
Das analytische, konzentrierte und präzise Denken der Studierenden wird geschult. Durch die eigenständige Behandlung von Anwendungen, z.B. im Rahmen der Übungsaufgaben, wird ihr Abstraktionsvermögen weiterentwickelt und der Transfer des erlernten Stoffes auf verwandte Fragestellungen gefördert.
Die Studierenden können Mithilfe aktueller Techniken und Theorien der modernen Kryptographie die Sicherheit von kryptographischen Verfahren einschätzen bzw. Sicherheitsaussagen entsprechend zu beurteilen. Weiterhin sind sie in der Lage, Sicherheitsziele zu erkennen und entsprechende Techniken einzusetzen, die in Kryptographie I nicht behandelt werden konnten.
Methodenkompetenz:
Den Studierenden sind in der Lage, geeignete Methoden zu Sicherheitsanalyse von kryptographischen Verfahren auszuwählen und einzusetzen. Dazu gehören bspw. die Wahl der passenden Sicherheitsmodelle, das Beweisen der Sicherheit aufgrund klar präzisierter Annahmen und die Analyse gegebener Verfahren. Insbesondere besitzen die Studierenden die Fähigkeit, die Sicherheitsargumente für existierende Verfahren zu verstehen und einzuschätzen und auf neue zu übertragen. Weiterhin können sie Techniken und Protokolle einsetzen, um Sicherheitsziele zu erreichen, die mit den in Kryptographie I besprochenen Verfahren noch nicht möglich waren.
Personale Kompetenz:
Das analytische, konzentrierte und präzise Denken der Studierenden wird geschult. Durch die eigenständige Behandlung von Anwendungen, z.B. im Rahmen der Übungsaufgaben, wird ihr Abstraktionsvermögen weiterentwickelt und der Transfer des erlernten Stoffes auf verwandte Fragestellungen gefördert.
Recommended requirement:
Examination achievement:
Schriftliche Klausur (90 Minuten) oder mündliche Prüfung (30 Minuten)
Instructor(s):
Prof. Dr. Frederik Armknecht
Description:
In der Vorlesung erfolgt eine kurze Zusammenstellung der wichtigsten kryptographischen Grundalgorithmen und der für die Vorlesung relevanten mathematischen, algorithmischen und informations- und komplexitätstheoretischen Grundlagen. Diese werden einerseits vertieft und andererseits erweitert. Behandelte Themen sind beispielsweise
- moderne Techniken der Kryptanalyse und daraus ableitbare Designkriterien für kryptographische Verfahren
- kryptographische Protokolle
- Sicherheitsbeweise
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAA 510 Introduction of partial differential equations (Lecture)
EN
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Attendance:
On-campus and online, live
Literature:
Script (online)
L.C. Evans: Partial Differential Equations
F. John: Partial Differential Equations
L.C. Evans: Partial Differential Equations
F. John: Partial Differential Equations
Examination achievement:
oral examination, 30 minutes
Instructor(s):
Dr. Ross Ogilvie, Prof. Dr. Martin Schmidt
Description:
- Basic notions of partial differential equations
- method of characteristics
- Laplace equations
- heat equations
- wave equation
MAA 519 Stochastic Calculus (Lecture)
EN
Course type:
Lecture
ECTS:
5.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Attendance:
Online, recorded
Instructor(s):
Prof. Dr. David Johannes Prömel
Description:
Brownian motion and martingales in continuous time, Stochastic integration and Ito formula, solution theory for stochastic differential equations (strong solutions, linear SDEs), change of measure (Girsanov theorem), martingale representation theorem
MAB 504 Mathematics and Information (Lecture)
EN
Course type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
4
Learning target:
Fachkompetenz:
Quantisierung von Information und inhaltliche Interpretation der entsprechenden Maße (MK1, MO2)
Verständnis für Möglichkeiten und Grenzen log-optimaler Anlagestrategien (MK2, MF1, MF2)
Verständnis für die Rolle der Linearen Algebra in der Informationssuche und der Klassifikation von Information (MK1, MK2, MF1, MF2)
Methodenkompetenz:
Umgang mit gängigen Informationsmaßen (MF2)
Datenkompression mit Huffman-Bäumen und mit Transformationen (MO2)
Berechnung log-optimaler und universeller Portfolios (MK2, MF1, MF2)
Berechnung von PageRank und verwandten Rängen (MK1, MK2, MF1, MF2)
Latente semantische Analyse via Singulärwertzerlegung (MK2, MF2)
Personale Kompetenz:
Fähigkeit, intuitiv gegebene Begriffe wie Information, optimale sichere Anlagestrategie, Wichtigkeit oder Ähnlichkeit von Dokumenten und Webseiten durch verschiedene Ansätze mathematisch zu modellieren und die Vor- und Nachteile der verschiedenen Möglichkeiten abzuschätzen (MK2, MF2, MO2, MO4)
Quantisierung von Information und inhaltliche Interpretation der entsprechenden Maße (MK1, MO2)
Verständnis für Möglichkeiten und Grenzen log-optimaler Anlagestrategien (MK2, MF1, MF2)
Verständnis für die Rolle der Linearen Algebra in der Informationssuche und der Klassifikation von Information (MK1, MK2, MF1, MF2)
Methodenkompetenz:
Umgang mit gängigen Informationsmaßen (MF2)
Datenkompression mit Huffman-Bäumen und mit Transformationen (MO2)
Berechnung log-optimaler und universeller Portfolios (MK2, MF1, MF2)
Berechnung von PageRank und verwandten Rängen (MK1, MK2, MF1, MF2)
Latente semantische Analyse via Singulärwertzerlegung (MK2, MF2)
Personale Kompetenz:
Fähigkeit, intuitiv gegebene Begriffe wie Information, optimale sichere Anlagestrategie, Wichtigkeit oder Ähnlichkeit von Dokumenten und Webseiten durch verschiedene Ansätze mathematisch zu modellieren und die Vor- und Nachteile der verschiedenen Möglichkeiten abzuschätzen (MK2, MF2, MO2, MO4)
Recommended requirement:
Examination achievement:
Mündliche Prüfung oder schriftliche Klausur
Instructor(s):
apl. Prof. Dr. Wolfgang Seiler
Description:
Shannons Entropie und abgeleitete Informationsmaße
Entropie und Datenkompression
Die Wettstrategie von Kelly
Log-optimale Portfolios
Universelle Portfolios
Vektorraummethoden in der Informationssuche
Matrixzerlegungen und latente semantische Analyse
PageRank und verwandte Verfahren
Entropie und Datenkompression
Die Wettstrategie von Kelly
Log-optimale Portfolios
Universelle Portfolios
Vektorraummethoden in der Informationssuche
Matrixzerlegungen und latente semantische Analyse
PageRank und verwandte Verfahren
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAC 503 Introduction to Extremes (Lecture)
DE
Course type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
German
Credit hours 1:
4
Learning target:
Fachkompetenz:
Grundkenntnisse Extremwerttheorie und der Anwendung (MK1)
Grundkenntnisse über regulär variierende Funktionen
Methodenkompetenz:
Schätzen von Modellparametern für und Vorhersage von extremen Ereignissen im Sinne der Extremwerttheorie (MK2)
Grundlegende Rechenverfahren in der Extremwerttheorie (MK1, MF3)
Personale Kompetenz:
Problembewusstsein für und qualifizierter Umgang mit Extremereignissen (MO3, MO4)
Kompetenz im Umgang mit nicht additiven Strukturen in der Stochastik (MF3, MO3)
Grundkenntnisse Extremwerttheorie und der Anwendung (MK1)
Grundkenntnisse über regulär variierende Funktionen
Methodenkompetenz:
Schätzen von Modellparametern für und Vorhersage von extremen Ereignissen im Sinne der Extremwerttheorie (MK2)
Grundlegende Rechenverfahren in der Extremwerttheorie (MK1, MF3)
Personale Kompetenz:
Problembewusstsein für und qualifizierter Umgang mit Extremereignissen (MO3, MO4)
Kompetenz im Umgang mit nicht additiven Strukturen in der Stochastik (MF3, MO3)
Recommended requirement:
Examination achievement:
mündliche Prüfung
Instructor(s):
Prof. Dr. Martin Schlather
Description:
univariate und multivariate Extremwerttheorie
Maxima von unabhängig und identisch verteilten Zufallsvariablen
max-stabile Verteilungen und ihre Anziehungsbereiche
max-unendlich oft teilbare Verteilungen; Spektral-Maß; Punktprozess-Darstellung; Charakteristiken
Schätzer für den extreme value index
Maxima von unabhängig und identisch verteilten Zufallsvariablen
max-stabile Verteilungen und ihre Anziehungsbereiche
max-unendlich oft teilbare Verteilungen; Spektral-Maß; Punktprozess-Darstellung; Charakteristiken
Schätzer für den extreme value index
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Markov Processes (Lecture)
EN
Course type:
Lecture
ECTS:
5
Course suitable for:
Master
Language of instruction:
English
Attendance:
Online, recorded
Recommended requirement:
Examination achievement:
Oral exam
Instructor(s):
Prof. Dr. Leif Döring, Prof. Dr. Martin Slowik
Description:
The topic of this lecture are Markov processes in continuous time which are an important class of stochastic processes. We also introduce operator semigroups, generators and stochastic equations which provide approaches to the characterisation of Markov processes. The theory will be illustrated with many examples. The course will cover a part of the following topics:
– Construction of stochastic processes (Theorem of Daniel-Kolmogorov)
– Stopping and optional times and stopped processes
– Markov processes and its properties (Markov property, strong Markov property, forward and backward equation)
– Construction of Markov processes via the transition function
– Semigroups of linear operators, resolvents and generators (Theorem of Hille-Yoshida) and its relation to Markov processes
– Relation between Markov processes and martingales (Dynkin martingale)
– functionals of Markov processes and partial differential equations
– Construction of stochastic processes (Theorem of Daniel-Kolmogorov)
– Stopping and optional times and stopped processes
– Markov processes and its properties (Markov property, strong Markov property, forward and backward equation)
– Construction of Markov processes via the transition function
– Semigroups of linear operators, resolvents and generators (Theorem of Hille-Yoshida) and its relation to Markov processes
– Relation between Markov processes and martingales (Dynkin martingale)
– functionals of Markov processes and partial differential equations
Model Driven Development (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will be familiar with the accepted best practices and technologies used in mainstream model-driven development as well as state-of-the-art modeling technologies emerging from research institutions.
Methodological competence:
Students will know how to apply modeling technologies in real-world projects.
Personal competence:
Students will have the capability to analyse, understand and model complex systems.
Students will be familiar with the accepted best practices and technologies used in mainstream model-driven development as well as state-of-the-art modeling technologies emerging from research institutions.
Methodological competence:
Students will know how to apply modeling technologies in real-world projects.
Personal competence:
Students will have the capability to analyse, understand and model complex systems.
Recommended requirement:
Examination achievement:
Written examination (90 minutes)
Instructor(s):
Prof. Dr. Colin Atkinson
Description:
The course focuses on the principles, practices and tools involved in advanced model-driven development. This includes established modelling standard languages (e. g. UML, ATL, OCL . . . ) and modelling infrastructures (e. g. MOF, EMF, etc. ) as well as leading edge, state-of-the-art modelling technologies (e. g. LML, PLM . . . ). Key topics addressed include –
- Multi-level modeling
- Meta-modeling
- Ontology engineering versus model engineering
- Model transformations
- Domain specific language definition and use
- Model creation and evolution best practices
- Model-driven software development
- Model checking and ontology validation
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Monte Carlo Methoden II (Lecture)
DE
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
German
Credit hours 1:
2
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Andreas Neuenkirch
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
MAC 509 Numerical Methods for Ordinary Differential Equations (Lecture)
EN
Course type:
Lecture
ECTS:
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Andreas Neuenkirch
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Reinforcement Learning – Coding (Lecture w/
EN
Course type:
Lecture w/
ECTS:
5.0
Course suitable for:
Master
Language of instruction:
English
Instructor(s):
Prof. Dr. Leif Döring, Prof. Dr. Simon Weißmann
Responsible AI: Conceptual Foundations, Methods and Applications (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Instructor(s):
Philipp Kellmeyer
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Seminar Kinetic Models (Seminar)
EN
Course type:
Seminar
ECTS:
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Instructor(s):
Prof. Li Chen
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Statistics for Data Scientists (Lecture)
EN
Course type:
Lecture
ECTS:
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
4
Attendance:
Live & on-campus
Instructor(s):
Prof. Ph. D. Marc Ratkovic
Description:
https://calendly.com/marcratkovic/office-hours-consulting
I will be blocking off some hours for open consulting, conversation, and research discussions on my calendly. Please feel free to sign up and set up a time. I've left the times in 15 minute blocks, but if you would like more time, you can sign up for multiple blocks. I look forward to our conversations.
I will be blocking off some hours for open consulting, conversation, and research discussions on my calendly. Please feel free to sign up and set up a time. I've left the times in 15 minute blocks, but if you would like more time, you can sign up for multiple blocks. I look forward to our conversations.
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Web Data Integration (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Learning target:
Expertise:
Students will be able to identify opportunities for employing Web data in business applications and will learn to select and apply appropriate techniques for integrating and cleansing Web data.
Methodological competence:
Students will be able to identify opportunities for employing Web data in business applications and will learn to select and apply appropriate techniques for integrating and cleansing Web data.
Methodological competence:
- Participants will acquire knowledge of the data integration process as well as the techniques that are used in each phase of the process.
- project organization skills
Personal competence:
- presentation skills
- team work skills.
Recommended requirement:
Examination achievement:
Written examination (90 minutes), project report, oral project presentation
Instructor(s):
Prof. Dr. Christian Bizer, Ralph Peeters, Alexander Brinkmann
Description:
The Web is developing from a medium for publishing textual documents into a medium for sharing structured data. In the course, students will learn how to integrate and cleanse data from this global data space for the later usage of the data within business applications. The course will cover the following topics:
- Heterogeneity and Distributedness
- The Data Integration Process
- Web Data Formats
- Schema Matching and Data Translation
- Identity Resolution
- Data Quality Assessment
- Data Fusion
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.