Foto: Staatliche Schlösser und Gärten Baden-Württemberg

Wirtschafts­mathematik und Wirtschafts­informatik (englisch)

Wirtschafts­informatik (Bachelor)

Data Mining (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
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
Empfohlene Voraussetzungen:
Literatur:
Foundations of Statistics, Practical Informatics I
Prüfungs­leistung:
Foundations of Statistics, Practical Informatics I
Lektor(en):
Christian Bizer
Beschreibung:
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)
Praktikum Software Engineering (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
5.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lernziel:
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.
Empfohlene Voraussetzungen:
Literatur:
Programmier­praktikum I, Praktische Informatik I, Programmierprakti-kum II, Algorithmen und Datenstrukturen
Prüfungs­leistung:
Programmier­praktikum I, Praktische Informatik I, Programmierprakti-kum II, Algorithmen und Datenstrukturen
Lektor(en):
Colin Atkinson
Beschreibung:
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 (Vorlesung mit Übung, englisch)
Vorlesungs­typ:
Vorlesung mit Übung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lernziel:
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.
Empfohlene Voraussetzungen:
Literatur:
No formal prerequisites. However, knowledge with respect to the content of the following lectures are suggested:
Praktische Informatik I und II, Lineare Algebra, Kenntnisse in Programmierung
Prüfungs­leistung:
No formal prerequisites. However, knowledge with respect to the content of the following lectures are suggested:
Praktische Informatik I und II, Lineare Algebra, Kenntnisse in Programmierung
Lektor(en):
Frederik Armknecht
Beschreibung:
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

Wirtschafts­informatik (Master)

Algorithmik (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
3
Lernziel:
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.
Empfohlene Voraussetzungen:
Literatur:
Praktische Informatik I, Algorithmen und Datenstrukturen, lineare Algebra, Statistik
Prüfungs­leistung:
Praktische Informatik I, Algorithmen und Datenstrukturen, lineare Algebra, Statistik
Lektor(en):
Matthias Krause
Beschreibung:
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
Computer Graphics (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
Expertise:
  • The students have a detailed understanding of the techniques involved in creating graphics. They are able to evaluate existing computer graphics algorithms.

Methodological competence:

  • Students understand the technical basis of computer graphic algorithms; they are able to explain the discussed techniques and to implement them (e.g., a ray tracer).

Personal competence:

  • Understanding of complex graphics problems; thorough judgment in the design and use of methods; can work efficiently in a team
Empfohlene Voraussetzungen:
Literatur:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Prüfungs­leistung:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Lektor(en):
Beschreibung:
  • Colors & light, raster images
  • Hardware: input and output devices (sensor, display), graphic board (GPU)
  • Signal processing for images (filter, sampling, aliasing, antialiasing)
  • Ray Tracing
  • Camera transformation, perspective
  • Shading, reflections, shadows, transparency, refraction
  • Graphics pipeline, rasterization
  • Image and video adaptation
  • Applications (video animation, computer games, virtual reality, CAD, simulations)
Computer Graphics (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
Expertise:
  • The students have a detailed understanding of the techniques involved in creating graphics. They are able to evaluate existing computer graphics algorithms.

Methodological competence:

  • Students understand the technical basis of computer graphic algorithms; they are able to explain the discussed techniques and to implement them (e.g., a ray tracer).

Personal competence:

  • Understanding of complex graphics problems; thorough judgment in the design and use of methods; can work efficiently in a team
Empfohlene Voraussetzungen:
Literatur:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Prüfungs­leistung:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Lektor(en):
Beschreibung:
  • Colors & light, raster images
  • Hardware: input and output devices (sensor, display), graphic board (GPU)
  • Signal processing for images (filter, sampling, aliasing, antialiasing)
  • Ray Tracing
  • Camera transformation, perspective
  • Shading, reflections, shadows, transparency, refraction
  • Graphics pipeline, rasterization
  • Image and video adaptation
  • Applications (video animation, computer games, virtual reality, CAD, simulations)
Computer Graphics (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
Expertise:
  • The students have a detailed understanding of the techniques involved in creating graphics. They are able to evaluate existing computer graphics algorithms.

Methodological competence:

  • Students understand the technical basis of computer graphic algorithms; they are able to explain the discussed techniques and to implement them (e.g., a ray tracer).

Personal competence:

  • Understanding of complex graphics problems; thorough judgment in the design and use of methods; can work efficiently in a team
Empfohlene Voraussetzungen:
Literatur:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Prüfungs­leistung:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Lektor(en):
Beschreibung:
  • Colors & light, raster images
  • Hardware: input and output devices (sensor, display), graphic board (GPU)
  • Signal processing for images (filter, sampling, aliasing, antialiasing)
  • Ray Tracing
  • Camera transformation, perspective
  • Shading, reflections, shadows, transparency, refraction
  • Graphics pipeline, rasterization
  • Image and video adaptation
  • Applications (video animation, computer games, virtual reality, CAD, simulations)
Data Mining (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
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
Empfohlene Voraussetzungen:
Literatur:
Foundations of Statistics, Practical Informatics I
Prüfungs­leistung:
Foundations of Statistics, Practical Informatics I
Lektor(en):
Christian Bizer
Beschreibung:
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 (Vorlesung mit Übung, englisch)
Vorlesungs­typ:
Vorlesung mit Übung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lernziel:
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
Empfohlene Voraussetzungen:
Literatur:
IE 500 Data Mining (recommended, not required), basic knowledge of linear algebra
Prüfungs­leistung:
IE 500 Data Mining (recommended, not required), basic knowledge of linear algebra
Lektor(en):
Rainer Gemulla
Beschreibung:
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 (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
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
Empfohlene Voraussetzungen:
Literatur:
IE 500 Data Mining, programming skills in Java
Prüfungs­leistung:
IE 500 Data Mining, programming skills in Java
Lektor(en):
Heiko Paulheim
Beschreibung:
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
Enterprise Architecture Modeling (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
Expertise:
After taking this course students will be familiar with the main ingredients of enterprise architectures and state-of-the-art tools/approaches for designing and modeling them.
Methodological competence:
Students will have the expertise needed to participate in the development of enterprise architecture modeling teams and will be familiar with common problems and pitfalls.
Personal competence:
With the acquired skills and know-how, students will be able to play a key role in enterprise architecture development, analysis and implementation.
Empfohlene Voraussetzungen:
Literatur:
None
Prüfungs­leistung:
None
Lektor(en):
Colin Atkinson
Beschreibung:
Enterprise architectures describe the organizing logic for business processes and IT infrastructure reflecting the integration and standardization requirements of a company's operating model. The operating model is the desired state of business process integration and business process standardization for delivering goods and services to customers. In this course students will become familiar with state-of-the-art enterprise modeling approaches and tools such as Zachmann, Archimate, TOGAF and RM-ODP.
Information Retrieval and Web Search (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
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.
Empfohlene Voraussetzungen:
Literatur:
Programming skills (Java/C++ preferred). Fundamental notions of linear algebra, probability theory, as well as algorithms and data structures.
Prüfungs­leistung:
Programming skills (Java/C++ preferred). Fundamental notions of linear algebra, probability theory, as well as algorithms and data structures.
Lektor(en):
Beschreibung:
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.
Knowledge Management: Principles and Technologies (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
Expertise:
The participants of this course learn about the challenges and opportunities for IT support in knowledge management. They will become acquaint with the technological foundations of knowledge management systems, including computational methods for searching, extracting, distributing and using knowledge in an organization. Methodological competence:
During this course, the students will acquire the competence for effectively using computational methods for knowledge acquisition, representation and maintenance. Personal competence:
By collaborating on practical and theoretical exercises the participants of this course learn how to effectively work in teams. They improve upon their presentation skills by presenting their solutions in the tutorials.
Empfohlene Voraussetzungen:
Literatur:
Java programming skills
Prüfungs­leistung:
Java programming skills
Lektor(en):
Simone Paolo Ponzetto
Beschreibung:
  • Information Retrieval
  • Text Mining and Information Extraction
  • Knowledge Repositories
  • Social Network Analysis
  • Crowdsourcing
Web Mining (Vorlesung mit Übung, englisch)
Vorlesungs­typ:
Vorlesung mit Übung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lernziel:
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
Empfohlene Voraussetzungen:
Literatur:
IE 500 Data Mining, programming skills in Java
Prüfungs­leistung:
IE 500 Data Mining, programming skills in Java
Lektor(en):
Christian Bizer , Simone Paolo Ponzetto
Beschreibung:
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

Wirtschafts­mathematik (Bachelor)

Data Mining (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
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
Empfohlene Voraussetzungen:
Literatur:
Foundations of Statistics, Practical Informatics I
Prüfungs­leistung:
Foundations of Statistics, Practical Informatics I
Lektor(en):
Christian Bizer
Beschreibung:
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)
MAA 508 Advanced Analysis (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
8.0 (Modul/e)
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Empfohlene Voraussetzungen:
Literatur:
Linear algebra I, Analysis I,II
Prüfungs­leistung:
Linear algebra I, Analysis I,II
Lektor(en):
Georgios Psaradakis , Li Chen
Beschreibung:
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).
Praktikum Software Engineering (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
5.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lernziel:
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.
Empfohlene Voraussetzungen:
Literatur:
Programmier­praktikum I, Praktische Informatik I, Programmierprakti-kum II, Algorithmen und Datenstrukturen
Prüfungs­leistung:
Programmier­praktikum I, Praktische Informatik I, Programmierprakti-kum II, Algorithmen und Datenstrukturen
Lektor(en):
Colin Atkinson
Beschreibung:
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 (Vorlesung mit Übung, englisch)
Vorlesungs­typ:
Vorlesung mit Übung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lernziel:
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.
Empfohlene Voraussetzungen:
Literatur:
No formal prerequisites. However, knowledge with respect to the content of the following lectures are suggested:
Praktische Informatik I und II, Lineare Algebra, Kenntnisse in Programmierung
Prüfungs­leistung:
No formal prerequisites. However, knowledge with respect to the content of the following lectures are suggested:
Praktische Informatik I und II, Lineare Algebra, Kenntnisse in Programmierung
Lektor(en):
Frederik Armknecht
Beschreibung:
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

Wirtschafts­mathematik (Master)

Algorithmik (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
3
Lernziel:
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.
Empfohlene Voraussetzungen:
Literatur:
Praktische Informatik I, Algorithmen und Datenstrukturen, lineare Algebra, Statistik
Prüfungs­leistung:
Praktische Informatik I, Algorithmen und Datenstrukturen, lineare Algebra, Statistik
Lektor(en):
Matthias Krause
Beschreibung:
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
Computer Graphics (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
Expertise:
  • The students have a detailed understanding of the techniques involved in creating graphics. They are able to evaluate existing computer graphics algorithms.

Methodological competence:

  • Students understand the technical basis of computer graphic algorithms; they are able to explain the discussed techniques and to implement them (e.g., a ray tracer).

Personal competence:

  • Understanding of complex graphics problems; thorough judgment in the design and use of methods; can work efficiently in a team
Empfohlene Voraussetzungen:
Literatur:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Prüfungs­leistung:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Lektor(en):
Beschreibung:
  • Colors & light, raster images
  • Hardware: input and output devices (sensor, display), graphic board (GPU)
  • Signal processing for images (filter, sampling, aliasing, antialiasing)
  • Ray Tracing
  • Camera transformation, perspective
  • Shading, reflections, shadows, transparency, refraction
  • Graphics pipeline, rasterization
  • Image and video adaptation
  • Applications (video animation, computer games, virtual reality, CAD, simulations)
Computer Graphics (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
Expertise:
  • The students have a detailed understanding of the techniques involved in creating graphics. They are able to evaluate existing computer graphics algorithms.

Methodological competence:

  • Students understand the technical basis of computer graphic algorithms; they are able to explain the discussed techniques and to implement them (e.g., a ray tracer).

Personal competence:

  • Understanding of complex graphics problems; thorough judgment in the design and use of methods; can work efficiently in a team
Empfohlene Voraussetzungen:
Literatur:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Prüfungs­leistung:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Lektor(en):
Beschreibung:
  • Colors & light, raster images
  • Hardware: input and output devices (sensor, display), graphic board (GPU)
  • Signal processing for images (filter, sampling, aliasing, antialiasing)
  • Ray Tracing
  • Camera transformation, perspective
  • Shading, reflections, shadows, transparency, refraction
  • Graphics pipeline, rasterization
  • Image and video adaptation
  • Applications (video animation, computer games, virtual reality, CAD, simulations)
Computer Graphics (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
Expertise:
  • The students have a detailed understanding of the techniques involved in creating graphics. They are able to evaluate existing computer graphics algorithms.

Methodological competence:

  • Students understand the technical basis of computer graphic algorithms; they are able to explain the discussed techniques and to implement them (e.g., a ray tracer).

Personal competence:

  • Understanding of complex graphics problems; thorough judgment in the design and use of methods; can work efficiently in a team
Empfohlene Voraussetzungen:
Literatur:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Prüfungs­leistung:
Good knowledge of a higher programming language (e.g., Java, C++), basic knowledge of linear algebra
Lektor(en):
Beschreibung:
  • Colors & light, raster images
  • Hardware: input and output devices (sensor, display), graphic board (GPU)
  • Signal processing for images (filter, sampling, aliasing, antialiasing)
  • Ray Tracing
  • Camera transformation, perspective
  • Shading, reflections, shadows, transparency, refraction
  • Graphics pipeline, rasterization
  • Image and video adaptation
  • Applications (video animation, computer games, virtual reality, CAD, simulations)
Data Mining (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
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
Empfohlene Voraussetzungen:
Literatur:
Foundations of Statistics, Practical Informatics I
Prüfungs­leistung:
Foundations of Statistics, Practical Informatics I
Lektor(en):
Christian Bizer
Beschreibung:
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 (Vorlesung mit Übung, englisch)
Vorlesungs­typ:
Vorlesung mit Übung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lernziel:
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
Empfohlene Voraussetzungen:
Literatur:
IE 500 Data Mining (recommended, not required), basic knowledge of linear algebra
Prüfungs­leistung:
IE 500 Data Mining (recommended, not required), basic knowledge of linear algebra
Lektor(en):
Rainer Gemulla
Beschreibung:
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 (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
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
Empfohlene Voraussetzungen:
Literatur:
IE 500 Data Mining, programming skills in Java
Prüfungs­leistung:
IE 500 Data Mining, programming skills in Java
Lektor(en):
Heiko Paulheim
Beschreibung:
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
Enterprise Architecture Modeling (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
Expertise:
After taking this course students will be familiar with the main ingredients of enterprise architectures and state-of-the-art tools/approaches for designing and modeling them.
Methodological competence:
Students will have the expertise needed to participate in the development of enterprise architecture modeling teams and will be familiar with common problems and pitfalls.
Personal competence:
With the acquired skills and know-how, students will be able to play a key role in enterprise architecture development, analysis and implementation.
Empfohlene Voraussetzungen:
Literatur:
None
Prüfungs­leistung:
None
Lektor(en):
Colin Atkinson
Beschreibung:
Enterprise architectures describe the organizing logic for business processes and IT infrastructure reflecting the integration and standardization requirements of a company's operating model. The operating model is the desired state of business process integration and business process standardization for delivering goods and services to customers. In this course students will become familiar with state-of-the-art enterprise modeling approaches and tools such as Zachmann, Archimate, TOGAF and RM-ODP.
Information Retrieval and Web Search (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
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.
Empfohlene Voraussetzungen:
Literatur:
Programming skills (Java/C++ preferred). Fundamental notions of linear algebra, probability theory, as well as algorithms and data structures.
Prüfungs­leistung:
Programming skills (Java/C++ preferred). Fundamental notions of linear algebra, probability theory, as well as algorithms and data structures.
Lektor(en):
Beschreibung:
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.
Knowledge Management: Principles and Technologies (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lernziel:
Expertise:
The participants of this course learn about the challenges and opportunities for IT support in knowledge management. They will become acquaint with the technological foundations of knowledge management systems, including computational methods for searching, extracting, distributing and using knowledge in an organization. Methodological competence:
During this course, the students will acquire the competence for effectively using computational methods for knowledge acquisition, representation and maintenance. Personal competence:
By collaborating on practical and theoretical exercises the participants of this course learn how to effectively work in teams. They improve upon their presentation skills by presenting their solutions in the tutorials.
Empfohlene Voraussetzungen:
Literatur:
Java programming skills
Prüfungs­leistung:
Java programming skills
Lektor(en):
Simone Paolo Ponzetto
Beschreibung:
  • Information Retrieval
  • Text Mining and Information Extraction
  • Knowledge Repositories
  • Social Network Analysis
  • Crowdsourcing
MAA 504 Partielle Differentialgleichungen (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
8.0 (Modul/e)
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lernziel:
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)
Empfohlene Voraussetzungen:
Literatur:
Analysis I, II, Lineare Algebra I, Einführung in die Wahrscheinlichkeits­theorie
Prüfungs­leistung:
Analysis I, II, Lineare Algebra I, Einführung in die Wahrscheinlichkeits­theorie
Lektor(en):
Li Chen
Beschreibung:
Elliptische Differenzialgleichungen
Funktionen­räume
Randwert­problem, Dirichlet­problem
Apriori Abschätzungen
MAA 508 Advanced Analysis (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
8.0 (Modul/e)
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Empfohlene Voraussetzungen:
Literatur:
Linear algebra I, Analysis I,II
Prüfungs­leistung:
Linear algebra I, Analysis I,II
Lektor(en):
Georgios Psaradakis , Li Chen
Beschreibung:
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 (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lektor(en):
MAC 501 Advanced Mathematical Finance (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lektor(en):
MAC 501 Advanced Mathematical Finance (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lektor(en):
MAC 507 Nichtlineare Optimierung (Vorlesung, englisch)
Vorlesungs­typ:
Vorlesung
ECTS:
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lektor(en):
Claudia Schillings
MAS 510 Diffusion Equations (Seminar, englisch)
Vorlesungs­typ:
Seminar
ECTS:
Kurs geeignet für:
Kurssprache:
englisch
SWS:
2
Lektor(en):
Li Chen
Web Mining (Vorlesung mit Übung, englisch)
Vorlesungs­typ:
Vorlesung mit Übung
ECTS:
6.0
Kurs geeignet für:
Kurssprache:
englisch
SWS:
4
Lernziel:
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
Empfohlene Voraussetzungen:
Literatur:
IE 500 Data Mining, programming skills in Java
Prüfungs­leistung:
IE 500 Data Mining, programming skills in Java
Lektor(en):
Christian Bizer , Simone Paolo Ponzetto
Beschreibung:
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