Photo credit: Staatliche Schlösser und Gärten Baden-Württemberg

Business Informatics and Mathematics (English)

Business Informatics (Bachelor)

Data Mining (Lecture, English)
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):
Christian Bizer
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)
Data Security (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Frederik Armknecht
Software Engineering Practical (Lecture, English)
Lecture type:
Lecture
ECTS:
5.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Fachkompetenz:
Kenntnisse der Schlüsseltechnologien der modernen Softwaretechnik, sowie der gängigen Software Entwicklungsprozesse. Dies umfasst insbesondere die Gebiete der System- und Anforderungsanalyse, An-wendungsdesign und Systemarchitektur, Implementierung, Validie-rung und Verifikation, Testen, Softwarequalität, Wartung und Wei-terentwicklung von Softwaresystemen.
Methodenkompetenz:
Die Fähigkeit große Softwaresysteme beschreiben, entwerfen und entwickeln zu können unter Berücksichtigung diverser Risiken, die in industriellen Großprojekten auftreten (bspw. Qualität, Kosten, unter-schiedliche Stakeholder, Termindruck, …).
Personale Kompetenz:
Fähigkeiten große Softwaresysteme im Team zu entwerfen, zu entwickeln / implementieren, zu testen und auszuliefern.
Fähigkeiten ein komplexes Themengebiet in schriftlicher und mündlicher Form klar und unmissverständlich wiederzugeben.
Recommended requirement:
Examination achievement:
schriftliche Ausarbeitung und entwickeltes System, Teammeetings (14 Meetings à max. 2 Stunden) und Kolloquia (3 Kolloquien à max. 30 Minuten), Praktische Prüfungen, Programmierprojekt(e)
Instructor(s):
Marcus Kessel
Description:
Die Veranstaltung befasst sich mit dem der Methoden und Techniken die für eine team-orientierte, ingenieurmäßige Entwicklung von nicht-trivialen Softwaresystemen erforderlich sind. Insbesondere sind dies:
  • Software-Entwicklungsprozesse
  • System- und Anforderungsanalyse
  • Anwendungsdesign und Systemarchitektur
  • Softwarequalität
  • Validierung, Verifikation und Testen
  • Wartung und Weiterentwicklung
Selected Topics in IT-Security (Lecture w/ Exercise, English)
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
This course aims to increase the security awareness of students and offers them a basic understanding with respect to a variety of interesting topics. After this course, students will be able to (1) learn about symmetric and asymmetric encryption schemes, (2) classify and describe vulnerabilities and protection mechanisms of popular network protocols, web protocols, and software systems (2) analyze / reason about basic protection mechanisms for modern OSs, software and hardware systems.
Recommended requirement:
Examination achievement:
Oral exam (30 minutes)
Instructor(s):
Frederik Armknecht
Description:
Background and Learning Objectives
 
The large-scale deployment of Internet-based services and the open nature of the Internet come alongside with the increase of security threats against existing services. As the size of the global network grows, the incentives of attackers to abuse the operation of online applications also increase and their advantage in mounting successful attacks becomes considerable.
 
These cyber-attacks often target the resources, availability, and operation of online services. In the recent years, a considerable number of online services such as Amazon, CNN, eBay, and Yahoo were hit by online attacks; the losses in revenues of Amazon and Yahoo were almost 1.1 million US dollars. With an increasing number of services relying on online resources, security becomes an essential component of every system.
 
Content Description
 
This lecture covers the security of computer, software systems, and tamper resistant hardware. The course starts with a basic introduction on encryption functions, spanning both symmetric and asymmetric encryption techniques, discusses the security of the current encryption standard AES and explains the concept of Zero-Knowledge proofs. The course then continues with a careful examination of wired and wireless network security issues, and web security threats and mechanisms. This part also extends to analysis of buffer overflows. Finally, the course also covers a set of selected security topics such as trusted computing and electronic voting.
 
Topics:
 
  • Encryption Schemes (Private Key vs. Public Key, Block cipher security) and Cryptographic Protocols
  • Cryptanalysis,e.g., side channel attacks
  • Network Security
  • Wireless Security
  • Web Security (SQL, X-Site Scripting)
  • Buffer Overflows
  • Malware & Botnets
  • Trusted computing
  • Electronic Voting
  • OS Security

Business Informatics (Master)

Algorithmics (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
3
Learning target:
Fachkompetenz:
Die Studierenden erlernen wichtige und anspruchsvolle Verfahren zur Lösung komplexer Probleme vorwiegend im Bereich der diskreten Optimierung und der Analyse der Verfahren.
Methodenkompetenz:
Anhand praktischer Probleme aus dem Bereich des  Operation Research erlernen sie wie man diese Probleme  abstrahiert und  mittels der erlernten Verfahren einer Lösung zuführt.
Personale Kompetenz:
Ihr analytisches, konzentriertes und präzises Denken wird  geschult. Durch die eigenständige Behandlung von Anwendungen z. B. aus dem Bereich Operations Research im Rahmen der Übungsaufgaben wird ihr Abstraktionsvermögen weiterentwickelt und der Transfer des erlernten Stoffes auf verwandte Fragestellungen gefördert. Durch die Auseinandersetzung mit der Thematik von P versus NP und der beispielhaften Behandlung von praktisch relevanten NP-vollständigen Problemen werden sie  sensibilisiert  für die Thematik der effizienten Lösbarkeit.
Recommended requirement:
Examination achievement:
Klausur, 90 Minuten
Instructor(s):
Matthias Krause
Description:
Aufbauend auf der Veranstaltung Algorithmen und Datenstrukturen werden fortgeschrittene Konzepte und Algorithmen unter Einbeziehung der Korrektheit und Kosten der Verfahren behandelt. Dabei stehen Fragestellungen, die einen Bezug zu wirtschaftswissenschaftlichen Anwendungen haben im Fokus. Besonderes Augenmerk liegt dabei auf der Abbildung von konkreten praktischen Problemen, auf den Konzepten und deren Lösung mittels der Algorithmen. Die Problematik der nicht effizient (P versus NP) berechenbaren Probleme wird diskutiert und Heuristiken für NP-vollständige Optimierungsprobleme behandelt. Behandelte Fragestellungen sind z. B.:
  • Netzwerke und Algorithmen auf Netzwerken, Max-flow, Min-cost,
  • Matching bipartit, non bipartit, gewichtete
  • Stabiles Heiratsproblem
  • Zuweisungsproblem
  • Touren in Graphen: Handelsreisender, Chinesischer Briefträger
  • SAT-Algorithmen
Algorithmics (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
3
Learning target:
Fachkompetenz:
Die Studierenden erlernen wichtige und anspruchsvolle Verfahren zur Lösung komplexer Probleme vorwiegend im Bereich der diskreten Optimierung und der Analyse der Verfahren.
Methodenkompetenz:
Anhand praktischer Probleme aus dem Bereich des  Operation Research erlernen sie wie man diese Probleme  abstrahiert und  mittels der erlernten Verfahren einer Lösung zuführt.
Personale Kompetenz:
Ihr analytisches, konzentriertes und präzises Denken wird  geschult. Durch die eigenständige Behandlung von Anwendungen z. B. aus dem Bereich Operations Research im Rahmen der Übungsaufgaben wird ihr Abstraktionsvermögen weiterentwickelt und der Transfer des erlernten Stoffes auf verwandte Fragestellungen gefördert. Durch die Auseinandersetzung mit der Thematik von P versus NP und der beispielhaften Behandlung von praktisch relevanten NP-vollständigen Problemen werden sie  sensibilisiert  für die Thematik der effizienten Lösbarkeit.
Recommended requirement:
Examination achievement:
Klausur, 90 Minuten
Instructor(s):
Matthias Krause
Description:
Aufbauend auf der Veranstaltung Algorithmen und Datenstrukturen werden fortgeschrittene Konzepte und Algorithmen unter Einbeziehung der Korrektheit und Kosten der Verfahren behandelt. Dabei stehen Fragestellungen, die einen Bezug zu wirtschaftswissenschaftlichen Anwendungen haben im Fokus. Besonderes Augenmerk liegt dabei auf der Abbildung von konkreten praktischen Problemen, auf den Konzepten und deren Lösung mittels der Algorithmen. Die Problematik der nicht effizient (P versus NP) berechenbaren Probleme wird diskutiert und Heuristiken für NP-vollständige Optimierungsprobleme behandelt. Behandelte Fragestellungen sind z. B.:
  • Netzwerke und Algorithmen auf Netzwerken, Max-flow, Min-cost,
  • Matching bipartit, non bipartit, gewichtete
  • Stabiles Heiratsproblem
  • Zuweisungsproblem
  • Touren in Graphen: Handelsreisender, Chinesischer Briefträger
  • SAT-Algorithmen
Data Mining (Lecture, English)
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):
Christian Bizer
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)
Data Mining and Matrices (Lecture w/ Exercise, English)
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
Knowledge of the techniques, opportunities, and applications of matrix decompositions in data mining
Methodological competence:
  • Apply matrix decompositions for data mining tasks
  • Analyze and interpret matrix decompositions

Personal competence:

  • writing skills
  • presentation skills
Recommended requirement:
Examination achievement:
Oral or written examination, homework assignments
Instructor(s):
Rainer Gemulla
Description:
Many data mining tasks operate on dyadic data, i.e., data involving two types of entities (e.g., users and products, objects and attributes, or points and coordinates); such data can be naturally represented in terms of a matrix. Matrix decompositions, with which we (approximately) represent the data matrix as a product of two (or more) factor matrices, can be used to perform many common data mining tasks. In this lecture, we explore the use of matrix decompositions in data mining, cover data mining tasks such as prediction, clustering and pattern mining, and application areas such as recommender systems and topic modelling.
Data Mining II (Lecture, English)
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 advanced techniques and applications of data mining.
Methodological competence:
  • Successful participants will be able to address advanced issues in data mining projects, conduct complex projects and develop applications in the data mining field.
  • project organization skills

Personal competence:

  • presentation skills
  • team work skills
Recommended requirement:
Examination achievement:
written examination (90 minutes), written project report, oral project presentation
Instructor(s):
Description:
Data mining deals with the discovery of patterns in data, and with making predictions for the future, based on observations of the past. This course covers advanced issues in data mining which need to be addressed when applying data mining methods in real world projects, including:
  • Data Preprocessing
  • Regression and Forecasting
  • Dimensionality Reduction
  • Anomaly Detection
  • Time Series Analysis
  • Parameter Tuning
  • Ensemble Learning
  • Online Learning
Data Security (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Frederik Armknecht
Higher Level Computer Vision (Lecture w/ Exercise, English)
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Expertise: The students have a detailed understanding of Computer Vision techniques. They can evaluate given Computer Vision algorithms.

Methodological competence: Students understand the technical basis of Computer Vision algorithms; they can explain the discussed methods and implement them.

Personal competence: Understanding complex Computer Vision problems; thorough judgment in the design and use of methods; can work efficiently in a team.
Recommended requirement:
Literature:
  • R. Szeliski: Computer Vision Algorithms and Applications, Springer, 2010. ISBN: 978-1-84882-934-3. (Online available: http://szeliski.org/Book/
  • D. Forsyth, J. Ponce: Computer Vision: A Modern Approach, Prentice Hall, 2nd edition, 2012. ISBN: 978-0136085928 (Online available: http://cmuems.com/excap/readings/forsyth-ponce-computer-vision-a-modern-approach.pdf
  • R. Hartley, A. Zisserman: Multiple View Geometry in Computer Vision, Cambridge University Press, 2nd edition, 2004.
Instructor(s):
Margret Keuper
Description:
Aim of module
  • Diffusion Filters, TV minimization
  • Image Segmentation
  • Combinatorial optimization
  • Spectral Clustering
  • Optical Flow
  • Video and Motion Segmentation
  • 3D Geometry (Camera Calibration, Stereo Reconstruction)
  • Structure from Motion
  • Deep Learning for Computer Vision
Information Retrieval and Web Search (Lecture, English)
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 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):
Goran Glavas
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.
Web Mining (Lecture w/ Exercise, English)
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 knowledge of the techniques, opportunities and applications of Web mining. Methodological competence:
  • Successful participants will be able to identify opportunities for mining knowledge from Web content, select and apply appropriate techniques and interpret the results.
  • project organization skills

Personal competence:

  • presentation skills
  • team work skills
Recommended requirement:
Examination achievement:
written examination (90 minutes), written project report, oral project presentation
Instructor(s):
Simone Paolo Ponzetto
Description:
The textual content as well as the structured data which is accessible on the Web has an enormous potential for being mined to derive knowledge about nearly any aspect of human life. The course covers advanced data mining techniques for extracting knowledge from Web content as a basis for business decisions and applications. The course will cover the following topics:
  • Goals and Principles of Web Mining
  • Gathering and Preprocessing Web Data
  • Social Network Analysis
  • Opinion Mining and Sentiment Analysis
  • Web Usage Mining
  • Executing Large Scale Web Mining Tasks

Business Mathematics (Bachelor)

Data Mining (Lecture, English)
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):
Christian Bizer
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)
Data Security (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Frederik Armknecht
MAA 508 Advanced Analysis (Lecture, English)
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Registration procedure:
test
Recommended requirement:
Examination achievement:
mündliche Prüfung
Instructor(s):
Li Chen , Georgios Psaradakis
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).
Software Engineering Practical (Lecture, English)
Lecture type:
Lecture
ECTS:
5.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Fachkompetenz:
Kenntnisse der Schlüsseltechnologien der modernen Softwaretechnik, sowie der gängigen Software Entwicklungsprozesse. Dies umfasst insbesondere die Gebiete der System- und Anforderungsanalyse, An-wendungsdesign und Systemarchitektur, Implementierung, Validie-rung und Verifikation, Testen, Softwarequalität, Wartung und Wei-terentwicklung von Softwaresystemen.
Methodenkompetenz:
Die Fähigkeit große Softwaresysteme beschreiben, entwerfen und entwickeln zu können unter Berücksichtigung diverser Risiken, die in industriellen Großprojekten auftreten (bspw. Qualität, Kosten, unter-schiedliche Stakeholder, Termindruck, …).
Personale Kompetenz:
Fähigkeiten große Softwaresysteme im Team zu entwerfen, zu entwickeln / implementieren, zu testen und auszuliefern.
Fähigkeiten ein komplexes Themengebiet in schriftlicher und mündlicher Form klar und unmissverständlich wiederzugeben.
Recommended requirement:
Examination achievement:
schriftliche Ausarbeitung und entwickeltes System, Teammeetings (14 Meetings à max. 2 Stunden) und Kolloquia (3 Kolloquien à max. 30 Minuten), Praktische Prüfungen, Programmierprojekt(e)
Instructor(s):
Marcus Kessel
Description:
Die Veranstaltung befasst sich mit dem der Methoden und Techniken die für eine team-orientierte, ingenieurmäßige Entwicklung von nicht-trivialen Softwaresystemen erforderlich sind. Insbesondere sind dies:
  • Software-Entwicklungsprozesse
  • System- und Anforderungsanalyse
  • Anwendungsdesign und Systemarchitektur
  • Softwarequalität
  • Validierung, Verifikation und Testen
  • Wartung und Weiterentwicklung
Selected Topics in IT-Security (Lecture w/ Exercise, English)
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
This course aims to increase the security awareness of students and offers them a basic understanding with respect to a variety of interesting topics. After this course, students will be able to (1) learn about symmetric and asymmetric encryption schemes, (2) classify and describe vulnerabilities and protection mechanisms of popular network protocols, web protocols, and software systems (2) analyze / reason about basic protection mechanisms for modern OSs, software and hardware systems.
Recommended requirement:
Examination achievement:
Oral exam (30 minutes)
Instructor(s):
Frederik Armknecht
Description:
Background and Learning Objectives
 
The large-scale deployment of Internet-based services and the open nature of the Internet come alongside with the increase of security threats against existing services. As the size of the global network grows, the incentives of attackers to abuse the operation of online applications also increase and their advantage in mounting successful attacks becomes considerable.
 
These cyber-attacks often target the resources, availability, and operation of online services. In the recent years, a considerable number of online services such as Amazon, CNN, eBay, and Yahoo were hit by online attacks; the losses in revenues of Amazon and Yahoo were almost 1.1 million US dollars. With an increasing number of services relying on online resources, security becomes an essential component of every system.
 
Content Description
 
This lecture covers the security of computer, software systems, and tamper resistant hardware. The course starts with a basic introduction on encryption functions, spanning both symmetric and asymmetric encryption techniques, discusses the security of the current encryption standard AES and explains the concept of Zero-Knowledge proofs. The course then continues with a careful examination of wired and wireless network security issues, and web security threats and mechanisms. This part also extends to analysis of buffer overflows. Finally, the course also covers a set of selected security topics such as trusted computing and electronic voting.
 
Topics:
 
  • Encryption Schemes (Private Key vs. Public Key, Block cipher security) and Cryptographic Protocols
  • Cryptanalysis,e.g., side channel attacks
  • Network Security
  • Wireless Security
  • Web Security (SQL, X-Site Scripting)
  • Buffer Overflows
  • Malware & Botnets
  • Trusted computing
  • Electronic Voting
  • OS Security

Business Mathematics (Master)

Algorithmics (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
3
Learning target:
Fachkompetenz:
Die Studierenden erlernen wichtige und anspruchsvolle Verfahren zur Lösung komplexer Probleme vorwiegend im Bereich der diskreten Optimierung und der Analyse der Verfahren.
Methodenkompetenz:
Anhand praktischer Probleme aus dem Bereich des  Operation Research erlernen sie wie man diese Probleme  abstrahiert und  mittels der erlernten Verfahren einer Lösung zuführt.
Personale Kompetenz:
Ihr analytisches, konzentriertes und präzises Denken wird  geschult. Durch die eigenständige Behandlung von Anwendungen z. B. aus dem Bereich Operations Research im Rahmen der Übungsaufgaben wird ihr Abstraktionsvermögen weiterentwickelt und der Transfer des erlernten Stoffes auf verwandte Fragestellungen gefördert. Durch die Auseinandersetzung mit der Thematik von P versus NP und der beispielhaften Behandlung von praktisch relevanten NP-vollständigen Problemen werden sie  sensibilisiert  für die Thematik der effizienten Lösbarkeit.
Recommended requirement:
Examination achievement:
Klausur, 90 Minuten
Instructor(s):
Matthias Krause
Description:
Aufbauend auf der Veranstaltung Algorithmen und Datenstrukturen werden fortgeschrittene Konzepte und Algorithmen unter Einbeziehung der Korrektheit und Kosten der Verfahren behandelt. Dabei stehen Fragestellungen, die einen Bezug zu wirtschaftswissenschaftlichen Anwendungen haben im Fokus. Besonderes Augenmerk liegt dabei auf der Abbildung von konkreten praktischen Problemen, auf den Konzepten und deren Lösung mittels der Algorithmen. Die Problematik der nicht effizient (P versus NP) berechenbaren Probleme wird diskutiert und Heuristiken für NP-vollständige Optimierungsprobleme behandelt. Behandelte Fragestellungen sind z. B.:
  • Netzwerke und Algorithmen auf Netzwerken, Max-flow, Min-cost,
  • Matching bipartit, non bipartit, gewichtete
  • Stabiles Heiratsproblem
  • Zuweisungsproblem
  • Touren in Graphen: Handelsreisender, Chinesischer Briefträger
  • SAT-Algorithmen
Algorithmics (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
3
Learning target:
Fachkompetenz:
Die Studierenden erlernen wichtige und anspruchsvolle Verfahren zur Lösung komplexer Probleme vorwiegend im Bereich der diskreten Optimierung und der Analyse der Verfahren.
Methodenkompetenz:
Anhand praktischer Probleme aus dem Bereich des  Operation Research erlernen sie wie man diese Probleme  abstrahiert und  mittels der erlernten Verfahren einer Lösung zuführt.
Personale Kompetenz:
Ihr analytisches, konzentriertes und präzises Denken wird  geschult. Durch die eigenständige Behandlung von Anwendungen z. B. aus dem Bereich Operations Research im Rahmen der Übungsaufgaben wird ihr Abstraktionsvermögen weiterentwickelt und der Transfer des erlernten Stoffes auf verwandte Fragestellungen gefördert. Durch die Auseinandersetzung mit der Thematik von P versus NP und der beispielhaften Behandlung von praktisch relevanten NP-vollständigen Problemen werden sie  sensibilisiert  für die Thematik der effizienten Lösbarkeit.
Recommended requirement:
Examination achievement:
Klausur, 90 Minuten
Instructor(s):
Matthias Krause
Description:
Aufbauend auf der Veranstaltung Algorithmen und Datenstrukturen werden fortgeschrittene Konzepte und Algorithmen unter Einbeziehung der Korrektheit und Kosten der Verfahren behandelt. Dabei stehen Fragestellungen, die einen Bezug zu wirtschaftswissenschaftlichen Anwendungen haben im Fokus. Besonderes Augenmerk liegt dabei auf der Abbildung von konkreten praktischen Problemen, auf den Konzepten und deren Lösung mittels der Algorithmen. Die Problematik der nicht effizient (P versus NP) berechenbaren Probleme wird diskutiert und Heuristiken für NP-vollständige Optimierungsprobleme behandelt. Behandelte Fragestellungen sind z. B.:
  • Netzwerke und Algorithmen auf Netzwerken, Max-flow, Min-cost,
  • Matching bipartit, non bipartit, gewichtete
  • Stabiles Heiratsproblem
  • Zuweisungsproblem
  • Touren in Graphen: Handelsreisender, Chinesischer Briefträger
  • SAT-Algorithmen
Applied Topology II (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Daniel Roggenkamp
Computational Finance (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Strauch
Data Mining (Lecture, English)
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):
Christian Bizer
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)
Data Mining and Matrices (Lecture w/ Exercise, English)
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
Knowledge of the techniques, opportunities, and applications of matrix decompositions in data mining
Methodological competence:
  • Apply matrix decompositions for data mining tasks
  • Analyze and interpret matrix decompositions

Personal competence:

  • writing skills
  • presentation skills
Recommended requirement:
Examination achievement:
Oral or written examination, homework assignments
Instructor(s):
Rainer Gemulla
Description:
Many data mining tasks operate on dyadic data, i.e., data involving two types of entities (e.g., users and products, objects and attributes, or points and coordinates); such data can be naturally represented in terms of a matrix. Matrix decompositions, with which we (approximately) represent the data matrix as a product of two (or more) factor matrices, can be used to perform many common data mining tasks. In this lecture, we explore the use of matrix decompositions in data mining, cover data mining tasks such as prediction, clustering and pattern mining, and application areas such as recommender systems and topic modelling.
Data Mining II (Lecture, English)
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 advanced techniques and applications of data mining.
Methodological competence:
  • Successful participants will be able to address advanced issues in data mining projects, conduct complex projects and develop applications in the data mining field.
  • project organization skills

Personal competence:

  • presentation skills
  • team work skills
Recommended requirement:
Examination achievement:
written examination (90 minutes), written project report, oral project presentation
Instructor(s):
Description:
Data mining deals with the discovery of patterns in data, and with making predictions for the future, based on observations of the past. This course covers advanced issues in data mining which need to be addressed when applying data mining methods in real world projects, including:
  • Data Preprocessing
  • Regression and Forecasting
  • Dimensionality Reduction
  • Anomaly Detection
  • Time Series Analysis
  • Parameter Tuning
  • Ensemble Learning
  • Online Learning
Data Security (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Frederik Armknecht
Higher Level Computer Vision (Lecture w/ Exercise, English)
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Expertise: The students have a detailed understanding of Computer Vision techniques. They can evaluate given Computer Vision algorithms.

Methodological competence: Students understand the technical basis of Computer Vision algorithms; they can explain the discussed methods and implement them.

Personal competence: Understanding complex Computer Vision problems; thorough judgment in the design and use of methods; can work efficiently in a team.
Recommended requirement:
Literature:
  • R. Szeliski: Computer Vision Algorithms and Applications, Springer, 2010. ISBN: 978-1-84882-934-3. (Online available: http://szeliski.org/Book/
  • D. Forsyth, J. Ponce: Computer Vision: A Modern Approach, Prentice Hall, 2nd edition, 2012. ISBN: 978-0136085928 (Online available: http://cmuems.com/excap/readings/forsyth-ponce-computer-vision-a-modern-approach.pdf
  • R. Hartley, A. Zisserman: Multiple View Geometry in Computer Vision, Cambridge University Press, 2nd edition, 2004.
Instructor(s):
Margret Keuper
Description:
Aim of module
  • Diffusion Filters, TV minimization
  • Image Segmentation
  • Combinatorial optimization
  • Spectral Clustering
  • Optical Flow
  • Video and Motion Segmentation
  • 3D Geometry (Camera Calibration, Stereo Reconstruction)
  • Structure from Motion
  • Deep Learning for Computer Vision
Information Retrieval and Web Search (Lecture, English)
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 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):
Goran Glavas
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.
MAA 504 Partial Differential Equations (Lecture, English)
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Fachkompetenz:
Vertrautheit mit den Grundbegriffen partieller Differenzialgleichungen (MK1)
Vertrautheit mit Distributionen, Hölderräumen und Sobolevräumen (MK1)
Vertrautheit mit Sobolevungleichungen (MK1)
Verständnis des Konzepts der schwachen Lösung (MK1, MO2)
Verständnis des Randverhaltens von Lösungen (MK1, MO2)
Methodenkompetenz:
Fähigkeit die Existenz von Lösungen zu untersuchen (MO2)
Fähigkeit die Eindeutigkeit von Lösungen zu untersuchen (MO2)
Fähigkeit die Regularität von Lösungen zu untersuchen (MO2)
Personale Kompetenz:
Vertieftes Verständnis für komplexe Argumentationen in der elliptischen Theorie (MO3)
Recommended requirement:
Examination achievement:
mündliche Prüfung
Instructor(s):
Martin Schmidt
Description:
Elliptische Differenzialgleichungen
Funktionenräume
Randwertproblem, Dirichletproblem
Apriori Abschätzungen
MAA 508 Advanced Analysis (Lecture, English)
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Registration procedure:
test
Recommended requirement:
Examination achievement:
mündliche Prüfung
Instructor(s):
Li Chen , Georgios Psaradakis
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).
MAC 501 Advanced Mathematical Finance (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Hours per week:
4
Instructor(s):
MAC 507 Nonlinear Optimization (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Hours per week:
2
Instructor(s):
Claudia Schillings
Markov Processes (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Leif Döring
SEM 510 Diffusion Equations (Seminar, English)
Lecture type:
Seminar
ECTS:
Course suitable for:
Language of instruction:
English
Hours per week:
2
Instructor(s):
Li Chen
Web Mining (Lecture w/ Exercise, English)
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 knowledge of the techniques, opportunities and applications of Web mining. Methodological competence:
  • Successful participants will be able to identify opportunities for mining knowledge from Web content, select and apply appropriate techniques and interpret the results.
  • project organization skills

Personal competence:

  • presentation skills
  • team work skills
Recommended requirement:
Examination achievement:
written examination (90 minutes), written project report, oral project presentation
Instructor(s):
Simone Paolo Ponzetto
Description:
The textual content as well as the structured data which is accessible on the Web has an enormous potential for being mined to derive knowledge about nearly any aspect of human life. The course covers advanced data mining techniques for extracting knowledge from Web content as a basis for business decisions and applications. The course will cover the following topics:
  • Goals and Principles of Web Mining
  • Gathering and Preprocessing Web Data
  • Social Network Analysis
  • Opinion Mining and Sentiment Analysis
  • Web Usage Mining
  • Executing Large Scale Web Mining Tasks