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Business Informatics and Mathematics in Business and Economics (all)

Information on your course choice

Please note that you have to take the majority of classes at the School of Business Informatics and Mathematics. In most cases you do not need to register for courses, please just attend the first lecture. In case you want to take courses outside from our school you can choose from the university wide electives list.
Good to know: undergraduate students are allowed to take graduate’s level courses at the School of Business Informatics and Mathematics. Partially, there are no requirements for participation in a Master’s course.

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Business Informatics (Bachelor)

Algorithms and Data Structures (Lecture)
DE
Lecture type:
Lecture
ECTS:
8.0
Course suitable for:
Language of instruction:
German
Hours per week:
6
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.
Recommended requirement:
Examination achievement:
Schriftliche Klausur (90 Minuten)
Instructor(s):
Matthias Krause
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 10:15 - 11:45 B 144 Hörsaal; A 5, 6 Bauteil B
Wednesday  (weekly) 09.09.2020 - 09.12.2020 12:00 - 13:30 B 144 Hörsaal; A 5, 6 Bauteil B
Thursday  (weekly) 10.09.2020 - 10.12.2020 17:15 - 18:45 B 144 Hörsaal; A 5, 6 Bauteil B
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…)
CS 560 Large-Scale Data Management (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Expertise:
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
Instructor(s):
Rainer Gemulla
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 10:15 - 11:45
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
Data Mining (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
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):
Heiko Paulheim
Date(s):
Wednesday  (weekly) 09.09.2020 - 09.12.2020 10:15 - 11:45
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)
Database Systems I (Lecture)
DE
Lecture type:
Lecture
ECTS:
8.0
Course suitable for:
Language of instruction:
German
Hours per week:
6
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
Recommended requirement:
Examination achievement:
schriftliche Klausur (90 Minuten)
Instructor(s):
Guido Moerkotte
Date(s):
Monday  (weekly) 14.09.2020 - 07.12.2020 12:00 - 13:30
Wednesday  (weekly) 09.09.2020 - 09.12.2020 10:15 - 11:45
Description:
Datenbankentwurf, Normalisierung, Anfragebearbeitung, Transaktionsverwaltung
Decision Support (Lecture w/ Exercise)
EN
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
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):
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 13:45 - 15:15
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
Decision Support (Lecture w/ Exercise)
EN
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
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):
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 15:30 - 17:00
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
Einführung in Data Science Vorlesung (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 10:15 - 11:45
Formal Foundations of Computer Science (Lecture)
DE
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
German
Hours per week:
4
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.
Recommended requirement:
Examination achievement:
Schriftliche Klausur (90 Minuten)
Instructor(s):
Matthias Krause
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 12:00 - 13:30 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A
Thursday  (weekly) 10.09.2020 - 10.12.2020 13:45 - 15:15 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A
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
Cryptographie I (Lecture w/ Exercise)
DE
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
German
Hours per week:
4
Learning target:
Fachkompetenz:
Nach Abschluss des Moduls  sind die Studierenden befähigt, die größten Risiken im elektronischen Datenverkehr, wie sie bspw. beim Online-Banking oder Einkauf über Online-Händler wie Amazon auftreten können, zu erkennen und zu vermeiden.
Methodenkompetenz:
Die Studierenden können  in konkreten Anwendungsfällen notwendige Sicherheitsziele erkennen und passende Methoden auswählen und einsetzen. Beispiele sind Verfahren zur Geheimhaltung von Daten (Verschlüsselungen), den Aufbau einer vertrauenswürdigen Verbindung (Schlüsselaustausch) und der sicheren Authentifikation (Zertifikate und digitale Signaturen).
Personale Kompetenz:
Das analytische, konzentrierte und präzise Denken der Studierenden wird geschult. Durch die eigenständige Behandlung von Anwendun-gen, z.B. im Rahmen der Übungsaufgaben, wird ihr Abstraktionsver-mögen weiterentwickelt und der Transfer des erlernten Stoffes auf verwandte Fragestellungen gefördert.
Recommended requirement:
Examination achievement:
Schriftliche (90 Minuten) oder mündliche Prüfung (30 Minuten)
Instructor(s):
Matthias Alexander Hamann
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 13:45 - 15:15
Thursday  (weekly) 10.09.2020 - 10.12.2020 13:45 - 15:15
Description:
In der Vorlesung erfolgt eine Einführung in die moderne Kryptographie, d.h. in die Theorie und der Praxis der Absicherung von digitalen Daten. Neben der Bereitstellung der für das Verständnis des Stoffs nötigen mathematischen, algorithmischen und informationstheoretischen Grundlagen werden vor allem die grundlegenden Konzepte und mehrere in der Praxis eingesetzte Verfahren vorgestellt.
 
Behandelt Themen sind beispielsweise:
  • Grundbegriffe der Kryptographie
  • Blockchiffren, z.B. Data Encryption Standard (DES) und Advanced Encryption Standard (AES), und Stromchiffren
  • Verfahren zum sicheren Schlüsselaustausch, bspw. das Diffie-Hellman Protokoll
  • Public-Key Verschlüsselungsverfahren, bspw. RSA
  • Hashfunktionen
  • Message Authentication Codes
Artificial Intelligence (Lecture)
DE
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
German
Hours per week:
4
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.
Recommended requirement:
Examination achievement:
Erfolgreiche Teilnahme am Übungsbetrieb
schriftliche Klausur (90 Minuten)
Instructor(s):
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 13:45 - 15:15
Description:
  • Problemeigenschaften und Problemtypen
  • Problemlösen als Suche, Anwendung im Bereich Computerspiele
  • Constraintprobleme und deren Lösung
  • Logische Constraints
Practical Computer Science I (Lecture)
DE
Lecture type:
Lecture
ECTS:
8.0
Course suitable for:
Language of instruction:
German
Hours per week:
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
Recommended requirement:
Examination achievement:
Studienbeginn ab HWS 2011:
Erfolgreiche Teilnahme am Übungsbetrieb
schriftliche Klausur (90 Minuten)

Studienbeginn vor HWS 2011:
schriftliche Klausur (90 Minuten)

Instructor(s):
Frederik Armknecht
Date(s):
Friday  (weekly) 11.09.2020 - 11.12.2020 15:30 - 17:00 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A
Thursday  (weekly) 10.09.2020 - 10.12.2020 15:30 - 17:00 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A
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
Programming Lab I (Lecture)
DE
Lecture type:
Lecture
ECTS:
5.0
Course suitable for:
Language of instruction:
German
Hours per week:
4
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):
Ursula Rost
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 08:30 - 10:00 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A
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:
 
  • 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.

Business Informatics (Master)

Advanced Software Engineering (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
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/component  – that is, for describing a software system/component as a “black box”, and for verifying them. Methodological competence:
Participants will know how to use the expertise acquired during the course to describe the requirements that a system/component has to satisfy and to define tests to check whether a system/component fulfils these requirements. Personal competence:
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):
Colin Atkinson
Date(s):
Friday  (weekly) 11.09.2020 - 11.12.2020 10:15 - 11:45 B 144 Hörsaal; A 5, 6 Bauteil B
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/components. Key examples include structural views represented using class diagrams, operational views expressed using constraint languages and behavioural views expressed using state diagrams. An important focus of the course is the use of these views to define tests and extra-functional properties.
CS 560 Large-Scale Data Management (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Expertise:
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
Instructor(s):
Rainer Gemulla
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 10:15 - 11:45
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
Data Mining (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
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):
Heiko Paulheim
Date(s):
Wednesday  (weekly) 09.09.2020 - 09.12.2020 10:15 - 11:45
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)
Decision Support (Lecture w/ Exercise)
EN
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
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):
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 13:45 - 15:15
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
Decision Support (Lecture w/ Exercise)
EN
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
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):
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 15:30 - 17:00
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
IS 614 Corporate Knowledge Management (Lecture w/ Exercise)
EN
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Registration procedure:
No registration required.
Learning target:
Course participants will be able to:
•    explain the role and importance of knowledge for organizations
•    Understand and explain the processes of knowledge management (KM)
•    Describe and evaluate the possibilities to support the different knowledge processes through information technology
•    Understand and evaluate different design principles of KM systems
•    Evaluate and apply organizational and technological mechanisms that ensure the use of KM systems
Examination achievement:
Written exam (60 min.); optional case study (20%)
Instructor(s):
Kai Spohrer
Date(s):
Monday  (block date) 05.10.2020 - 16.10.2020 17:15 - 18:45 O 142 Engelhorn Hörsaal; Schloss Ostflügel
Thursday  (single date) 05.11.2020 15:30 - 17:00 O 142 Engelhorn Hörsaal; Schloss Ostflügel
Thursday  (single date) 12.11.2020 15:30 - 17:00 O 142 Engelhorn Hörsaal; Schloss Ostflügel
Description:
Companies have realized that the knowledge of their professionals is a decisive factor in competition. Firms are able to differentiate against their competitors through superior knowledge in the long term.
This lecture deals with the question of how the creation acquisition, transfer, storage, retrieval, and use of knowledge can be supported with the information technology and where the limits of such efforts are. It also addresses how to design information technology to support different knowledge processes.
IT-Security (Seminar)
EN
Lecture type:
Seminar
ECTS:
4.0
Course suitable for:
Language of instruction:
English
Instructor(s):
Frederik Armknecht
Date(s):
Thursday  (weekly) 10.09.2020 - 10.12.2020 13:45 - 15:15
Multimodal Content Analysis for Media and Communication Science (Seminar)
EN
Lecture type:
Seminar
ECTS:
4.0
Course suitable for:
Language of instruction:
English
Learning target:
Expertise:
The student gains a deep understanding of the research topic. He/she is able to describe/summarize the topic in detail in his/her own words. He/she reflects on the topic and judges the contribution of the research papers.
Methodological competence:
The student is able to find the relevant literature for his/her topic, to write a well-structured scientific paper and to present his/her results. He/she is also aware of the need to avoid plagiarism.
Personal qualification:
The student has learned how to find relevant literature for a research topic, write a well-structured, concise paper about it and give a presentation. He/she is well prepared to write and present a Master’s Thesis.
Recommended requirement:
Examination achievement:
Seminar paper, oral presentation and the participation in the group discussions
Instructor(s):
Heiner Stuckenschmidt
Description:
Media and communication sciences deal with processes of human communication, from face-to-face conversation to mass media outlets such as television broadcasting. Communication studies also examines how messages are interpreted through the political, cultural, economic and social dimensions of their contexts. Analyzing media content is an important research method in this context.
 
In the course of this seminar, participants will learn basic methods and techniques for carrying out analyses of media in different modalities (text, still images, video, audio) as a basis for answering research questions in media and communication science and investigate inhowfar these methods can be automated using state of the art techniques from Artificial Intelligence, in particular image and video processing and natural language understanding.
Model Driven Development (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
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.
Recommended requirement:
Examination achievement:
Written examination (90 minutes)
Instructor(s):
Colin Atkinson
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 13:45 - 15:15
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
Network Analysis (Lecture w/ Exercise)
EN
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Instructor(s):
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 12:00 - 13:30
Network Analysis (Lecture w/ Exercise)
EN
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Instructor(s):
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 13:45 - 15:15
Semantic Web Technologies (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
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.
Recommended requirement:
Examination achievement:
Regular exercises, team project, written examination (90 minutes)
Instructor(s):
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 13:45 - 15:15
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
Text Analytics (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
Students will acquire knowledge of state-of-the-art principles and methods of Natural Language Processing, with a specific focus on the application of statistical methods to human language technologies.
Methodological competence:
Successful participants will be able to understand state-of-the-art methods for Natural Language Processing, as well as being able to select, apply and evaluate the most appropriate techniques for a variety of different practical and application-oriented scenarios.
Personal competence:
  • presentation skills;
  • team work skills.
Recommended requirement:
Examination achievement:
Written examination (90 minutes), homework assignments
Instructor(s):
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 15:30 - 17:00
Description:
In the digital age, techniques to automatically process textual content have become ubiquitous. Given the breakneck speed at which people produce and consume textual content online – e.g., on micro-blogging and other collaborative Web platforms like wikis, forums, etc. – there is an ever-increasing need for systems that automatically understand human language, answer natural language questions, translate text, and so on. This class will provide a complete introduction to state-of-the-art principles and methods of Natural Language Processing (NLP). The main focus will be on statistical techniques, and their application to a wide variety of problems. This is because statistics and NLP are nowadays highly intertwined, since many NLP problems can be formulated as problems of statistical inference, and statistical methods, in turn, represent de-facto the standard way  to solve many, if not the majority, of NLP problems. Covered topics will include:
 
  • Words
    • Language Modeling
    • Part-Of-Speech Tagging
  • Syntax
    • Statistical Parsing
  • Semantics and pragmatics
    • Computational Lexical Semantics
    • Computational Discourse
  • Applications
    • Topic Modeling
    • Information Extraction
    • Question Answering and Summarization
    • Statistical Alignment and Machine Translation
Coursework will include homework assignments and a final exam. Homework assignments are meant to introduce the students to the problems that will be covered in the final exam at the end of the course.
Web Data Integration (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
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:
  • 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):
Date(s):
Wednesday  (weekly) 09.09.2020 - 09.12.2020 15:30 - 17:00
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
Web Data Integration (Lecture)
EN
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
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:
  • 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):
Date(s):
Thursday  (weekly) 10.09.2020 - 10.12.2020 10:15 - 11:45
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

Business Mathematics (Bachelor)

MAA 404 Functional Analysis (Lecture)
DE
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
German
Hours per week:
4
Learning target:
Fachkompetenz:
• Wegintegrale im Komplexen (BK1)
• Potenzreihenkalkül (BK1)
• Fundamentalsatz der Algebra (BK1)
• Cauchyscher Integralsatz und Integralformel (BF1, BK1)
• Residuensatz (BK1, BO3)
Methodenkompetenz:
• Zusammenhang zwischen reeller und komplexer Differenzierbarkeit (BF1, BO2)
• Berechnen von Residuen (BO3)
• Berechnen von reellen Integralen mit dem Residuensatz (BF1, BO3)
• Verständnis von lokalen und globalen Eigenschaften holomorpher Funktionen (BF1, BO2)
Personale Kompetenz:
• Teamarbeit (BF4)
Recommended requirement:
Examination achievement:
Mündliche Prüfung oder schriftliche Klausur
Instructor(s):
Peter Parczewski
Date(s):
Thursday  (weekly) 10.09.2020 - 10.12.2020 12:00 - 13:30
Friday  (weekly) 11.09.2020 - 11.12.2020 10:15 - 11:45
Description:
• Komplexe Differenzierbarkeit
• holomorphe und meromorphe Funktionen
• Analytische Fortseztung
• Singularitäten holomorpher Funktionen
• Residuenkalkül
• spezielle Funktionen
MAA 508 Advanced Analysis (Lecture)
EN
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Recommended requirement:
Examination achievement:
mündliche Prüfung
Instructor(s):
Georgios Psaradakis
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 10:15 - 11:45
Friday  (weekly) 11.09.2020 - 11.12.2020 12:00 - 13:30
Description:
This course will start with basic knowledge of real analysis, includes measure and integration, then we will go into some advanced topics in analysis, such as L^p spaces, distributions, the Fourier transform, Sobolev spaces and related inequalities. These are necessary knowledge in modern PDE theories and their applications (for example, in physics, biology and economy).
MAA 510 Introduction of partial differential equations (Lecture)
EN
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Martin Schmidt
Date(s):
Wednesday  (weekly) 09.09.2020 - 09.12.2020 08:30 - 10:00
Thursday  (weekly) 10.09.2020 - 10.12.2020 15:30 - 17:00
MAC 404 Optimization (Lecture)
DE
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
German
Hours per week:
4
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)
Recommended requirement:
Examination achievement:
Mündliche Prüfung oder schriftliche Klausur
Instructor(s):
Andreas Neuenkirch
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 10:15 - 11:45 B 144 Hörsaal; A 5, 6 Bauteil B
Wednesday  (weekly) 09.09.2020 - 09.12.2020 10:15 - 11:45 B 144 Hörsaal; A 5, 6 Bauteil B
Tuesday  (weekly) 08.09.2020 - 08.12.2020 10:15 - 11:45
Wednesday  (weekly) 09.09.2020 - 09.12.2020 10:15 - 11:45
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
MAT 301 Analysis I (Lecture)
DE
Lecture type:
Lecture
ECTS:
10.0 (Modul/e)
Course suitable for:
Language of instruction:
German
Hours per week:
4
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)
Recommended requirement:
Examination achievement:
schriftliche Klausur
Instructor(s):
Martin Schmidt
Date(s):
Wednesday  (weekly) 09.09.2020 - 09.12.2020 12:00 - 13:30 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A
Friday  (weekly) 11.09.2020 - 11.12.2020 10:15 - 11:45 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A
Description:
• Mengen und Abbildungen
• reelle Zahlen
• Zahlenfolgen und Reihen
• Funktionen in einer reellen Variablen
MAT 303 Linear Algebra I (Lecture)
DE
Lecture type:
Lecture
ECTS:
9.0 (Modul/e)
Course suitable for:
Language of instruction:
German
Hours per week:
4
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).
Recommended requirement:
Examination achievement:
schriftliche Klausur
Instructor(s):
Daniel Roggenkamp
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 10:15 - 11:45 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A
Thursday  (weekly) 10.09.2020 - 10.12.2020 08:30 - 10:00 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A
Description:
• Gruppen, Ringe, Körper, Vektorräume, Lineare Abbildungen, Matrizen, Lineare Gleichungssysteme, Determinanten,  Eigenwerte und Diagonalisierung,  Euklidische Vektorräume.
MAT 306 Numerical Mathematics (Lecture)
DE
Lecture type:
Lecture
ECTS:
9.0
Course suitable for:
Language of instruction:
German
Hours per week:
4
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)
Recommended requirement:
Examination achievement:
schriftliche Klausur
Instructor(s):
Simone Göttlich
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 12:00 - 13:30 B 243 Hörsaal; A 5, 6 Bauteil B
Tuesday  (weekly) 08.09.2020 - 08.12.2020 12:00 - 13:30 B 144 Hörsaal; A 5, 6 Bauteil B
Description:
• Numerik linearer Gleichungssysteme
• Störungstheorie und Fehleranalyse
• Lineare Ausgleichsrechnung
• Eigenwertprobleme
• Nichtlineare Gleichungssysteme: Fixpunktiterationen, insbesondere Newton-Verfahren
• Interpolation und Splines
• Numerische Integration
Mathematische Methoden der Big Data Analytics 1 (Lecture)
DE
Lecture type:
Lecture
ECTS:
8.0
Course suitable for:
Language of instruction:
German
Instructor(s):
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 08:30 - 10:00
Thursday  (weekly) 10.09.2020 - 10.12.2020 08:30 - 10:00
Stochastik 1 (Lecture)
DE
Lecture type:
Lecture
ECTS:
9.0
Course suitable for:
Language of instruction:
German
Instructor(s):
Leif Döring
Date(s):
Tuesday  (weekly) 08.09.2020 - 08.12.2020 13:45 - 15:15 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A
Thursday  (weekly) 10.09.2020 - 10.12.2020 12:00 - 13:30 A 001 Großer Hörsaal; B 6, 23-25 Bauteil A

Business Mathematics (Master)

MAA 508 Advanced Analysis (Lecture)
EN
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Recommended requirement:
Examination achievement:
mündliche Prüfung
Instructor(s):
Georgios Psaradakis
Date(s):
Monday  (weekly) 07.09.2020 - 11.12.2020 10:15 - 11:45
Friday  (weekly) 11.09.2020 - 11.12.2020 12:00 - 13:30
Description:
This course will start with basic knowledge of real analysis, includes measure and integration, then we will go into some advanced topics in analysis, such as L^p spaces, distributions, the Fourier transform, Sobolev spaces and related inequalities. These are necessary knowledge in modern PDE theories and their applications (for example, in physics, biology and economy).
MAA 510 Introduction of partial differential equations (Lecture)
EN
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Martin Schmidt
Date(s):
Wednesday  (weekly) 09.09.2020 - 09.12.2020 08:30 - 10:00
Thursday  (weekly) 10.09.2020 - 10.12.2020 15:30 - 17:00
MAC 503 Introduction to Extremes (Lecture)
DE
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
German
Hours per week:
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)
Recommended requirement:
Examination achievement:
mündliche Prüfung
Instructor(s):
Date(s):
Wednesday  (weekly) 09.09.2020 - 09.12.2020 10:15 - 11:45
Thursday  (weekly) 10.09.2020 - 10.12.2020 10:15 - 11:45
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
MAC 509 Numerical Methods for Ordinary Differential Equations (Lecture)
DE
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
German
Instructor(s):
Simone Göttlich
Date(s):
Thursday  (weekly) 10.09.2020 - 10.12.2020 13:45 - 15:15
MAC 508 Computational SDEs (Lecture)
DE
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
German
Hours per week:
2
Learning target:
Fachkompetenz: Die Studierenden haben die grundlegenden Fragestellungen  und wichtigsten Methoden der Numerik stochastischer Differentialgleichungen erlernt, insbesondere die    Unterschiede zwischen den verschiedenen Approximationsbegriffen, das Euler- und Milsteinverfahren  sowie Multi-level Monte-Carlo-Verfahren (MK1,M02).
Methodenkompetenz: Die Studierenden können nach Besuch des Moduls gegebene numerische Probleme für stochastische Differentialgleichungen klassifizieren und zur Bearbeitung geeignete Verfahren auswählen bzw. konstruieren (MF1,MF2,MO3).
Personale Kompetenz: Teamarbeit
Recommended requirement:
Examination achievement:
mündliche Prüfung
Instructor(s):
Peter Parczewski
Date(s):
Wednesday  (weekly) 09.09.2020 - 09.12.2020 12:00 - 13:30
Description:
Theoretische Grundlagen: stochastische Prozesse; stochastische Integration und stochastische Differentialgleichungen.
Numerik: Simulation von Gaußprozessen; Fehlerbegriffe; Klassische Approximationsverfahren; Cameron-Clark Theorem; Quadratur von SDGLn; Anwendungen in Technik und Finanzmathematik
Seminar Modellierung, Numerik und Optimierung (Seminar)
Lecture type:
Seminar
ECTS:
4.0
Course suitable for:
Language of instruction:
Instructor(s):
Simone Göttlich , Andreas Neuenkirch , Claudia Schillings

Contact School of Business Informatics and Mathematics

Juliane Roth, M.A.

Juliane Roth, M.A.

Departmental Exchange Coordinator
University of Mannheim
School of Business Informatics and Mathematics
B 6, 26
Gebäudeteil B – Room B 1.05
68159 Mannheim
Phone: +49 621 181-2340
Fax: +49 621 181-2423
E-mail: roth(at)wim.uni-mannheim.de
Consultation hour(s):
by appointment via email