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Das Mannheimer Barockschloss und der Ehrenhof unter blauem Himmel.
Credit: Staatliche Schlösser und Gärten Baden-Württemberg

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Please note that you have to take the majority of classes at the School of Business Informatics and Mathematics.

To start the registration procedure, please click on ‘More information’ below to be redirected to Portal2. Please sign up for all courses directly on the Portal2. For seminars and Team Projects please follow the respective notes on the websites of your lecturers. The registration period differs for courses with limited and open capacity.

If you're an undergraduate student, you might take graduate-level courses at the School of Business, Informatics and Mathematics. However, please pay attention to any prerequisites.

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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.
Recommended requirement:
Examination achievement:
Schriftliche Klausur (90 Minuten)
Instructor(s):
Prof. Dr. Matthias Krause
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202610:15 – 11:45A 001 Großer Hörsaal; B 6, 23–25 Bauteil A
Wednesday  (weekly) 09.09.2026 – 09.12.202610:15 – 11:45B 144 Hörsaal; A 5, 6 Bauteil B
Thursday  (weekly) 10.09.2026 – 10.12.202617:15 – 18:45B 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…)
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
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):
Prof. Dr. Guido Moerkotte
Date(s):
Wednesday  (weekly) 09.09.2026 – 09.12.202610:15 – 11:45A 101 Kleiner Hörsaal; B 6, 23–25 Bauteil A
Monday  (weekly) 14.09.2026 – 07.12.202612:00 – 13:30A 101 Kleiner Hörsaal; B 6, 23–25 Bauteil A
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.
Formal Foundations of Computer Science (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:
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
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202612:00 – 13:30A 001 Großer Hörsaal; B 6, 23–25 Bauteil A
Thursday  (weekly) 10.09.2026 – 10.12.202613:45 – 15:15A 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
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Foundations of Artificial Intelligence – Reasoning and Decision Making (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:
  • 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
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202612:00 – 13:30C 014 Hörsaal; A 5, 6 Bauteil C
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.
Foundations of Artificial Intelligence – Reasoning and Decision Making (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:
  • 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
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202613:45 – 15:15C 014 Hörsaal; A 5, 6 Bauteil C
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.
Foundations of Artificial Intelligence – Reasoning and Decision Making (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:
  • 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
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202615:30 – 17:00C 013 Hörsaal; A 5, 6 Bauteil C
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.
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 will be possible from 17 August 2026. 

To register for this course, you must provide a confirmation of completion of the following courses, each with a workload of 6 ECTS or 2 contact hours per week:
– Foundations of ERP Systems
- Introduction to Operations Management
- Management Information Systems
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
Date(s):
Friday  (weekly) 23.10.2026 – 04.12.202610:15 – 13:30SN 169 Röchling Hörsaal; Schloss Schneckenhof Nord
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.
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.

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):
Prof. Dr. Rainer Gemulla
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202615:30 – 17:00A 101 Kleiner Hörsaal; B 6, 23–25 Bauteil A
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
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
Language of instruction:
German
Credit hours 1:
6
Attendance:
Live & on-campus
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):
Prof. Dr. Frederik Armknecht
Date(s):
Thursday  (weekly) 10.09.2026 – 10.12.202615:30 – 17:00A 001 Großer Hörsaal; B 6, 23–25 Bauteil A
Friday  (single date) 02.10.202615:30 – 17:00
Friday  (single date) 23.10.202615:30 – 17:00
Friday  (single date) 13.11.202615:30 – 17:00
Friday  (single date) 04.12.202615:30 – 17:00
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
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
Date(s):
Thursday  (weekly) 10.09.2026 – 10.12.202612:00 – 13:30A 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.
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 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:
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
Instructor(s):
Prof. Dr. Armin Heinzl
Date(s):
Friday  (weekly) 11.09.2026 – 16.10.202610:15 – 13:30SN 169 Röchling Hörsaal; Schloss Schneckenhof Nord
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.
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
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.
Recommended requirement:
Examination achievement:
Written exam (30%) (90 Minuten)
Case study write-up (70%)
Instructor(s):
Prof. Dr. Hartmut Höhle
Date(s):
Thursday  (weekly) 10.09.2026 – 10.12.202612:00 – 13:30O 148 MVV Hörsaal; Schloss Ostflügel
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.
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 Software Engineering (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
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):
Prof. Dr. Colin Atkinson
Date(s):
Friday  (weekly) 11.09.2026 – 11.12.202610:15 – 11:45B 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.
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
Attendance:
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.
Recommended requirement:
Examination achievement:
Schriftliche Klausur (90 Minuten) oder mündliche Prüfung (30 Minuten)
Instructor(s):
Prof. Dr. Frederik Armknecht
Date(s):
Friday  (weekly) 11.09.2026 – 11.12.202608:30 – 10:00A 203 Unterrichtsraum; B 6, 23–25 Bauteil A
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.
CS 735 Computational Analysis of Communication (Lecture)
EN
Course type:
Lecture
ECTS:
4.0
Course suitable for:
Master
Language of instruction:
English
Attendance:
Instructor(s):
Dr. Rainer Freudenthaler
More information
Data Mining (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:
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:
  • 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):
Dr. Sven Hertling
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202613:45 – 15:15A 001 Großer Hörsaal; B 6, 23–25 Bauteil A
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.
Foundations of Artificial Intelligence – Reasoning and Decision Making (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:
  • 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
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202612:00 – 13:30C 014 Hörsaal; A 5, 6 Bauteil C
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.
Foundations of Artificial Intelligence – Reasoning and Decision Making (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:
  • 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
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202613:45 – 15:15C 014 Hörsaal; A 5, 6 Bauteil C
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.
Foundations of Artificial Intelligence – Reasoning and Decision Making (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:
  • 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
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202615:30 – 17:00C 013 Hörsaal; A 5, 6 Bauteil C
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 (Lecture w/ Exercise)
EN
Course type:
Lecture w/ Exercise
ECTS:
6.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202612:00 – 13:30C 013 Hörsaal; A 5, 6 Bauteil C
Thursday  (weekly) 10.09.2026 – 10.12.202613:45 – 15:15A 101 Kleiner Hörsaal; B 6, 23–25 Bauteil A
More information
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
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202610:15 – 11:45A 101 Kleiner Hörsaal; B 6, 23–25 Bauteil A
Thursday  (weekly) 10.09.2026 – 10.12.202610:15 – 11:45A 101 Kleiner Hörsaal; B 6, 23–25 Bauteil A
More information
Information Retrieval and Web Search (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Learning target:
Expertise:
Students will acquire knowledge of fundamental techniques of Information Retrieval and Web Search, including standard retrieval models, evaluation of information retrieval systems, text classification and clustering, as well as web search topics such as crawling and link-based algorithms.
Methodological competence:
Successful participants will be able to understand state-of-the-art methods for Information Retrieval and Web search, as well as being able to select, apply and evaluate the most appropriate techniques for a variety of different search scenarios.
Personal competence:
  • presentation skills;
  • team work skills.
Recommended requirement:
Examination achievement:
Written examination (90 minutes), written project report, oral project presentation
Instructor(s):
Prof. Dr. Simone Paolo Ponzetto, Dr. Daniel Ruffinelli
Date(s):
Wednesday  (weekly) 09.09.2026 – 09.12.202612:00 – 13:30A 101 Kleiner Hörsaal; B 6, 23–25 Bauteil A
Description:
Given the vastness and richness of the Web, users need high-performing, scalable and efficient methods to access its wealth of information and satisfy their information needs. As such, being able to search and effectively retrieve relevant pieces of information from large text collections is a crucial task for the majority (if practically not all) of Web applications. In this course we will explore a variety of basic and advanced techniques for text-based information retrieval and Web search. Covered topics will include:
 
  • Efficient text indexing;
  • Boolean and vector space retrieval models;
  • Evaluation of retrieval systems;
  • Probabilistic Information Retrieval;
  • Text classification and clustering;
  • Web search, crawling and link-based algorithms.

Coursework will include homework assignments, a term project 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. In addition, students are expected to successfully complete a term project in teams of 2–4 people. The projects will focus on a variety of IR problems covered in class. Project deliverables include both software (i.e., code and documentation) and a short report explaining the work performed and its evaluation.
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 613 Applied Project in Design Thinking and Lean Software Development (Lecture w/ Exercise)
EN
Course type:
Lecture w/ Exercise
ECTS:
6.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
Attendance:
Date(s):
Friday  (weekly) 16.10.2026 – 13.11.202615:30 – 17:00ZOOM-Lehre-018; Virtuelles Gebäude
Description:
Please find a detailed course description via the following link:
Module Catalog MMM | Universität Mannheim (uni-mannheim.de)
More information
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
Instructor(s):
Dr. Deborah Mateja
Date(s):
Thursday  (weekly) 10.09.2026 – 03.12.202610:15 – 11:45O 151 Hans Luik Hörsaal; Schloss Ostflügel
Description:
Please find a detailed course description via the following link:
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 661 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
Instructor(s):
Indira Sen
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202612:00 – 13:30O 148 MVV Hörsaal; Schloss Ostflügel
Description:
Please find a detailed course description via the following link:
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
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Frederik Armknecht
More information
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.

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):
Prof. Dr. Rainer Gemulla
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202615:30 – 17:00A 101 Kleiner Hörsaal; B 6, 23–25 Bauteil A
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
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Master Seminar Computer Vision (Seminar)
EN
Course type:
Seminar
ECTS:
4.0
Course suitable for:
Master
Language of instruction:
English
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202617:15 – 18:45A 104 Seminarraum; B 6, 23–25 Bauteil A
More information
Model Driven Development (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
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):
Prof. Dr. Colin Atkinson
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202613:45 – 15:15C 014 Hörsaal; A 5, 6 Bauteil C
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:
  • 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, Dr. Ralph Peeters
Date(s):
Wednesday  (weekly) 09.09.2026 – 09.12.202615:30 – 17:00C 014 Hörsaal; A 5, 6 Bauteil C
Thursday  (weekly) 10.09.2026 – 10.12.202613:45 – 15:15C 013 Hörsaal; A 5, 6 Bauteil C
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 Mathematics (Bachelor)

MAA 401 Analysis III (Lecture)
DE
Course type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
Learning target:
Fachkompetenz:
• Karte und Atlas (BK1, BF1)
• Tangentialraum (BK1)
• Integralkurven von Vektorfeldern (BK1)
• Tensoren (BK1)
• Äußeres Produkt und äußere Ableitung von Differenzialformen (BK1, BO2)
• Der Satz von Stokes (BK1)
Methodenkompetenz:
• Verstehen des Transformationsverhaltens unter Kartenwechsel (BF1)
• Rechnen mit Tensoren (BF1)
• Bestimmung von Integralkurven (BF1, BF2)
• Hantieren mit Differenzialformen (BF1)
Personale Kompetenz:
• Teamarbeit (BO1, BF4)
Recommended requirement:
Examination achievement:
Mündliche Prüfung oder schriftliche Klausur
Instructor(s):
Prof. Dr. Martin Schmidt
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202613:45 – 15:15C 012 Seminarraum; A 5, 6 Bauteil C
Wednesday  (weekly) 09.09.2026 – 09.12.202608:30 – 10:00C 012 Seminarraum; A 5, 6 Bauteil C
Description:
• Differenzierbare Mannigfaltigkeit
• Vektorfelder
• gewöhnliche Differenzialgleichungen
• Differenzialformen
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 405 Cryptology (Lecture)
DE
Course type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
On-campus and online, live
Learning target:
Fachkompetenz:
• Verständnis für die vielfältigen Einsatzmöglichkeiten von Kryptoverfahren (BF2, BF4, BF5, BO1)
• Realistische Einschätzung der Sicherheit (BF1, BF3, BO2)
• Zahlentheoretische Grundlagen der Kryptographie mit öffentlichen Schlüsseln und von AES  (BK1, BK3, BO3)
• Vor- und Nachteile der Verfahren mit öffentlichen und privaten Schlüsseln; hybride Verfahren wie SSL/TLS (BK3, BF4, BF5)
• Verständnis für die konstruktive und die destruktive Rolle quantenmechanischer Verfahren (BF3, BF4)
Methodenkompetenz:
• Sicherer Einsatz von RSA zur Verschlüsselung und für elektronische Unterschriften (einschließlich Primzahlsuche) (BK1, BK3, BF3, BO3)
• Kenntnis der für RSA kritischen Faktorisierungsverfahren und der wichtigsten sonstigen Angriffsmöglichkeiten (BF1, BF2)
• Faktorisierung mit Quantencomputern (BF1, BO3)
• Verständnis von AES (BK1, BK3, BF1, BF3)
• Umgang mit diskreten Logarithmen, DSS (BK1, BK3, BF3)
• Grundlegende Protokolle der Quantenkryptographie (BF3, BO2)
Personale Kompetenz:
• Problembewusstsein für die Verwundbarkeit von Kryptosystemen und Fähigkeit zur rationalen Auswahl einer in Aufwand und Sicherheit dem jeweiligen Problem angemessenen Lösung (BO1, BO2, BO3)
Recommended requirement:
Examination achievement:
Mündliche Prüfung oder schriftliche Klausur
Instructor(s):
apl. Prof. Dr. Wolfgang Seiler
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202613:45 – 15:15A 104 Seminarraum; B 6, 23–25 Bauteil A
Wednesday  (weekly) 09.09.2026 – 09.12.202613:45 – 15:15A 104 Seminarraum; B 6, 23–25 Bauteil A
Description:
• Aufgaben der Kryptologie
• Klassische Kryptosysteme und ihre Kryptanalyse
• Feistel-Netzwerke und DES
• Differentielle und lineare Kryptanalyse; DES-Cracker
• New directions in cryptography
• RSA und seine zahlentheoretischen Grundlagen
• Faktorisierungsalgorithmen und andere Angriffe
• Verfahren auf der Grundlage diskreter Logarithmen
• Sichere Hashalgorithmen
• Kryptographische Protokolle
• Quantenkryptographie und Quantencomputer
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:
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):
Prof. Ph. D. Mathias Staudigl
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202608:30 – 10:00
Tuesday  (weekly) 08.09.2026 – 08.12.202615:30 – 17:00
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
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 410 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)
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
Instructor(s):
Dr. Peter Parczewski
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202612:00 – 13:30C 012 Seminarraum; A 5, 6 Bauteil C
Wednesday  (weekly) 09.09.2026 – 09.12.202612:00 – 13:30A 203 Unterrichtsraum; B 6, 23–25 Bauteil A
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
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 301 Analysis I (Lecture)
DE
Course type:
Lecture
ECTS:
10.0 (Modul/e)
Course suitable for:
Bachelor
Language of instruction:
German
Credit hours 1:
4
Attendance:
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):
Prof. Dr. Simone Rademacher
Date(s):
Wednesday  (weekly) 09.09.2026 – 09.12.202612:00 – 13:30A 001 Großer Hörsaal; B 6, 23–25 Bauteil A
Friday  (weekly) 11.09.2026 – 11.12.202610:15 – 11:45A 001 Großer Hörsaal; B 6, 23–25 Bauteil A
Description:
• Mengen und Abbildungen
• 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
Language of instruction:
German
Credit hours 1:
4
Attendance:
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):
Prof. Ph. D. Mathias Staudigl
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202610:15 – 11:45A 001 Großer Hörsaal; B 6, 23–25 Bauteil A
Thursday  (weekly) 10.09.2026 – 10.12.202608:30 – 10:00A 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.
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
Language of instruction:
German
Credit hours 1:
4
Attendance:
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):
Prof. Dr. Andreas Neuenkirch
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202612:00 – 13:30B 144 Hörsaal; A 5, 6 Bauteil B
Tuesday  (weekly) 08.09.2026 – 08.12.202612:00 – 13:30A 001 Großer Hörsaal; B 6, 23–25 Bauteil A
Description:
• Numerik linearer Gleichungssysteme
• 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.
SEM 440 Seminar (Seminar)
DE
Course type:
Seminar
ECTS:
3.0
Course suitable for:
Bachelor, Master
Language of instruction:
German
Credit hours 1:
2
Instructor(s):
Prof. Ph. D. Mathias Staudigl
Date(s):
Wednesday  (single date) 25.11.202608:30 – 20:00
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Mean-field particle systems and their limits to non-local PDEs This semester: Workload: Self-study (Lecture)
EN
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Instructor(s):
Yueying Yang
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202613:45 – 15:15C 401 Seminarraum; B 6, 27–29 Bauteil C
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Stochastik 1 (Lecture)
DE
Course type:
Lecture
ECTS:
9.0
Course suitable for:
Bachelor
Language of instruction:
German
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Simon Weißmann
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202613:45 – 15:15A 001 Großer Hörsaal; B 6, 23–25 Bauteil A
Thursday  (weekly) 10.09.2026 – 10.12.202612:00 – 13:30B 144 Hörsaal; A 5, 6 Bauteil B
More information

Business Mathematics (Master)

MAA 580 Methods in Analysis (Lecture)
EN
Course type:
Lecture
ECTS:
4.0 (Modul/e)
Course suitable for:
Master
Language of instruction:
English
More information
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
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
More information
SEM 440 Seminar (Seminar)
DE
Course type:
Seminar
ECTS:
3.0
Course suitable for:
Bachelor, Master
Language of instruction:
German
Credit hours 1:
2
Instructor(s):
Prof. Ph. D. Mathias Staudigl
Date(s):
Wednesday  (single date) 25.11.202608:30 – 20:00
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
Mean-field particle systems and their limits to non-local PDEs This semester: Workload: Self-study (Lecture)
EN
Course type:
Lecture
ECTS:
8.0
Course suitable for:
Bachelor, Master
Language of instruction:
English
Credit hours 1:
2
Attendance:
Live & on-campus
Instructor(s):
Yueying Yang
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202613:45 – 15:15C 401 Seminarraum; B 6, 27–29 Bauteil C
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
Attendance:
Instructor(s):
Prof. Dr. Simone Göttlich
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202610:15 – 11:45A 104 Seminarraum; B 6, 23–25 Bauteil A
Thursday  (weekly) 10.09.2026 – 10.12.202613:45 – 15:15A 104 Seminarraum; B 6, 23–25 Bauteil A
More information
Optimization in ML (Lecture)
EN
Course type:
Lecture
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Attendance:
Live & on-campus
Instructor(s):
Prof. Dr. Leif Döring
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202612:00 – 13:30C 013 Hörsaal; A 5, 6 Bauteil C
Thursday  (weekly) 10.09.2026 – 10.12.202612:00 – 13:30C 013 Hörsaal; A 5, 6 Bauteil C
More information
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
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202612:00 – 13:30C 401 Seminarraum; B 6, 27–29 Bauteil C
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.

Mannheim Master in Data Science

Master Seminar Computer Vision (Seminar)
EN
Course type:
Seminar
ECTS:
4.0
Course suitable for:
Master
Language of instruction:
English
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202617:15 – 18:45A 104 Seminarraum; B 6, 23–25 Bauteil A
More information
Reinforcement Learning (Lecture w/ Exercise)
EN
Course type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Master
Language of instruction:
English
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202610:15 – 11:45C 015 Hörsaal; A 5, 6 Bauteil C
Monday  (weekly) 07.09.2026 – 07.12.202612:00 – 13:30C 015 Hörsaal; A 5, 6 Bauteil C
More information
Seminar and Lab on Machine Learning and Causal Inference (Seminar)
EN
Course type:
Seminar
ECTS:
Course suitable for:
Master
Language of instruction:
English
Credit hours 1:
4
Attendance:
Live & on-campus
Instructor(s):
Prof. Ph. D. Marc Ratkovic
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202613:45 – 15:15A 301 Seminarraum; B 6, 23–25 Bauteil A
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
Date(s):
Tuesday  (weekly) 08.09.2026 – 08.12.202610:15 – 11:45D 002 Seminarraum 1; B 6, 27–29 Bauteil D
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.
VL Sampling and Data (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. Ph. D. Marc Ratkovic
Date(s):
Monday  (weekly) 07.09.2026 – 07.12.202608:30 – 10:00D 007 Seminarraum 2; B 6, 27–29 Bauteil D
More information
1 Credit hours indicate the duration of a course which is offered weekly during one semester. One credit hour equals 45 minutes.

Contact School of Business Informatics and Mathematics

Juliane Roth, M.A.
Credit: Juliane Roth

Juliane Roth, M.A. (she/her)

Departmental Exchange Coordinator, Digitization
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: juliane.rothuni-mannheim.de
Consultation hour(s):
by appointment via email

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