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Business Informatics and Mathematics (English)

Business Informatics (Bachelor)

Bachelorseminar Multimodal Mobile Affective Computing (Seminar, English)
Lecture type:
Seminar
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
CS 560 Large-Scale Data Management (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 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:
Literature:
Very good knowledge of database systems, good knowledge of algorithms and data structures as well as Java programming
Examination achievement:
Very good knowledge of database systems, good knowledge of algorithms and data structures as well as Java programming
Instructor(s):
Rainer Gemulla
Description:
This course introduces the fundamental concepts and computational paradigms of large-scale data management and Big Data. This includes methods for storing, updating, querying, and analyzing large dataset as well as for data-intensive computing. The course covers concept, algorithms, and system issues; accompanying exercises provide hands-on experience. Topics include:
• Parallel and distributed databases
• MapReduce and its ecosystem
• NoSQL
• Stream processing
• Graph databases
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:
Literature:
Foundations of Statistics, Practical Informatics I
Examination achievement:
Foundations of Statistics, Practical Informatics I
Instructor(s):
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 (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:
Literature:
Foundations of Statistics, Practical Informatics I
Examination achievement:
Foundations of Statistics, Practical Informatics I
Instructor(s):
Description:
The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:
  • Goals and Principles of Data Mining
  • Data Representation and Preprocessing
  • Clustering
  • Classification
  • Association Analysis
  • Text Mining
  • Systems and Applications (e. g. Retail, Finance, Web Analysis)
Decision Support (Lecture w/ Exercise, 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 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:
Literature:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Examination achievement:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Instructor(s):
Melisachew Wudage Chekol , Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
  • Decision Theory
  • Decision- and Business Rules
  • Planning Methods and Algorithms
  • Probabilistic Graphical Models
  • Game Theory and Mechanism Design
Decision Support (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 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:
Literature:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Examination achievement:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Instructor(s):
Melisachew Wudage Chekol , Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
  • Decision Theory
  • Decision- and Business Rules
  • Planning Methods and Algorithms
  • Probabilistic Graphical Models
  • Game Theory and Mechanism Design
Decision Support (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 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:
Literature:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Examination achievement:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Instructor(s):
Melisachew Wudage Chekol , Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
  • Decision Theory
  • Decision- and Business Rules
  • Planning Methods and Algorithms
  • Probabilistic Graphical Models
  • Game Theory and Mechanism Design
Einführung in Data Science Vorlesung (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
2
Instructor(s):
Simone Paolo Ponzetto
Relational Learning (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Instructor(s):
Heiner Stuckenschmidt

Business Informatics (Master)

Advanced Software Engineering (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
After taking the course, students will be familiar with the latest state-of-the-art techniques for specifying the externally visible properties of a software system/component  – that is, for describing a software system/component as a “black box”, and for verifying them. Methodological competence:
Participants will know how to use the expertise acquired during the course to describe the requirements that a system/component has to satisfy and to define tests to check whether a system/component fulfils these requirements. Personal competence:
With the acquired skills and know-how, students will be able to play a key role in projects involving the development of systems, components and software applications.
Recommended requirement:
Literature:
None
Examination achievement:
None
Instructor(s):
Colin Atkinson
Description:
The course deals with the model-based specification of software systems and components as well as their verification, validation and quality assurance. The emphasis is on view-based specification methods that use multiple views, expressed in multiple languages, to describe orthogonal aspects of software systems/components. Key examples include structural views represented using class diagrams, operational views expressed using constraint languages and behavioural views expressed using state diagrams. An important focus of the course is the use of these views to define tests and extra-functional properties.
CS 560 Large-Scale Data Management (Lecture, 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 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:
Literature:
Very good knowledge of database systems, good knowledge of algorithms and data structures as well as Java programming
Examination achievement:
Very good knowledge of database systems, good knowledge of algorithms and data structures as well as Java programming
Instructor(s):
Rainer Gemulla
Description:
This course introduces the fundamental concepts and computational paradigms of large-scale data management and Big Data. This includes methods for storing, updating, querying, and analyzing large dataset as well as for data-intensive computing. The course covers concept, algorithms, and system issues; accompanying exercises provide hands-on experience. Topics include:
• Parallel and distributed databases
• MapReduce and its ecosystem
• NoSQL
• Stream processing
• Graph databases
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:
Literature:
Foundations of Statistics, Practical Informatics I
Examination achievement:
Foundations of Statistics, Practical Informatics I
Instructor(s):
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 (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:
Literature:
Foundations of Statistics, Practical Informatics I
Examination achievement:
Foundations of Statistics, Practical Informatics I
Instructor(s):
Description:
The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:
  • Goals and Principles of Data Mining
  • Data Representation and Preprocessing
  • Clustering
  • Classification
  • Association Analysis
  • Text Mining
  • Systems and Applications (e. g. Retail, Finance, Web Analysis)
Decision Support (Lecture w/ Exercise, 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 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:
Literature:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Examination achievement:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Instructor(s):
Melisachew Wudage Chekol , Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
  • Decision Theory
  • Decision- and Business Rules
  • Planning Methods and Algorithms
  • Probabilistic Graphical Models
  • Game Theory and Mechanism Design
Decision Support (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 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:
Literature:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Examination achievement:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Instructor(s):
Melisachew Wudage Chekol , Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
  • Decision Theory
  • Decision- and Business Rules
  • Planning Methods and Algorithms
  • Probabilistic Graphical Models
  • Game Theory and Mechanism Design
Decision Support (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 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:
Literature:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Examination achievement:
Basic Probability Theory, Basic Knowledge of Propositional and First-Order Logic
Instructor(s):
Melisachew Wudage Chekol , Heiner Stuckenschmidt
Description:
The course provides an introduction to decision support techniques as a basis for the design of decision support systems. The course will cover the following topics:
  • Decision Theory
  • Decision- and Business Rules
  • Planning Methods and Algorithms
  • Probabilistic Graphical Models
  • Game Theory and Mechanism Design
IS 613 Applied Project in Design Thinking and Lean Software Development (Lecture w/ Exercise, English)
Lecture type:
Lecture w/ Exercise
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
2
Registration procedure:
To register for this course, please follow the instructions on the chair's website. Registration is possible between August 15 and September 2, 2018.
Learning target:
By the end of the module students will
•    know how to apply design principles for developing customer oriented applications,
•    understand the difficulties involved in team-based software development,
•    improve software engineering skills,
•    improve the ability to work in teams,
•    use state of the art software engineering methods and tools.
Recommended requirement:
Literature:
Formal: IS 615 (parallel attendance possible)
Recommended: This course is designed for master students of management or information systems. A basic understanding of how to program information systems is helpful.
Examination achievement:
Formal: IS 615 (parallel attendance possible)
Recommended: This course is designed for master students of management or information systems. A basic understanding of how to program information systems is helpful.
Instructor(s):
Philipp Günther Hoffmann
Description:
The goal of this term project is to collaboratively develop a concept, design or software to solve a real world problem in a student development team environment. We offer a project-based lecture with hands-on experience for lean principles and design thinking. Students will learn innovative product and process design for software development which then can be directly applied in exercise sessions. 
The used technology will depend on students’ skills and experience. Prototypes might be developed with technology for mobile devices or paper-based for non-developers.
IS 614 Corporate Knowledge Management (Lecture w/ Exercise, English)
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Registration procedure:
No registration required.
Learning target:
Course participants will be able to:
•    explain the role and importance of knowledge for organizations
•    Understand and explain the processes of knowledge management (KM)
•    Describe and evaluate the possibilities to support the different knowledge processes through information technology
•    Understand and evaluate different design principles of KM systems
•    Evaluate and apply organizational and technological mechanisms that ensure the use of KM systems
Recommended requirement:
Literature:
Formal: -
Recommended: -
Examination achievement:
Formal: -
Recommended: -
Instructor(s):
Kai Spohrer
Description:
Companies have realized that the knowledge of their professionals is a decisive factor in competition. Firms are able to differentiate against their competitors through superior knowledge in the long term.
This lecture deals with the question of how the creation acquisition, transfer, storage, retrieval, and use of knowledge can be supported with the information technology and where the limits of such efforts are. It also addresses how to design information technology to support different knowledge processes.
IS 615 Design Thinking and Lean Development in Enterprise Software Development (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
2
Registration procedure:
To register for this course, please follow the instructions on the chair's website. Registration is possible between August 15th and September 2nd, 2018.
Learning target:
After completing the class, students will be able to:
  • Understand the issues and challenges involved in enterprise software development
  • Understand and apply large-scale agile development based on lean principles
  • Understand and apply Design Thinking and related innovation practices
  • Understand and evaluate business models for software companies and products
  • Understand and apply how to bring all of this together in enterprise reality
  • Understand and evaluate state of the art software engineering methods
  • Understand and explain particular success strategies recommended by practitioners
  • Understand how to launch a start-up and scale a software company
Recommended requirement:
Literature:
Formal: -
Recommended: IS 550. Attendance of IS 613 in the same semester is recommended.
Examination achievement:
80% written exam (60 min), 20% case study
Instructor(s):
Anna-Maria Seeger
Description:
Enterprise software development revolves around complex and interdependent software products for different companies, lines of business and industries. Hence, there is an inherent trade-off between standard software and domain-specific software solutions. Software companies thus have to keep track of various heterogeneous and possibly conflicting market requirements that are subject to changes and updates in ever shorter release cycles.
However, it is essential for every enterprise software company to be able to build the right solutions efficiently. To be able to do so in the long run, large software companies elaborated good practices to ensure both, efficient development processes and innovative products.
Among these, lean thinking and agile software development practices combined with Design Thinking and related practices are increasingly adopted and intertwined in the software industry. So, the goal of this module is to convey both, approaches from research and industry experience together with practical application based on concrete enterprise software challenges. The course includes both, lecture and workshop formats.

A combination with IS 613 as related term project is highly recommended.
Cryptographie II (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Fachkompetenz:
Die Studierenden können Mithilfe aktueller Techniken und Theorien der modernen Kryptographie die Sicherheit von kryptographischen Verfahren einschätzen bzw. Sicherheitsaussagen entsprechend zu beurteilen. Weiterhin sind sie in der Lage, Sicherheitsziele zu erkennen und entsprechende Techniken einzusetzen, die in Kryptographie I nicht behandelt werden konnten.
Methodenkompetenz:
Den Studierenden sind in der Lage, geeignete Methoden zu Sicherheitsanalyse von kryptographischen Verfahren auszuwählen und einzusetzen. Dazu gehören bspw. die Wahl der passenden Sicherheitsmodelle, das Beweisen der Sicherheit aufgrund klar präzisierter Annahmen und die Analyse gegebener Verfahren. Insbesondere besitzen die Studierenden die Fähigkeit, die Sicherheitsargumente für existierende Verfahren zu verstehen und einzuschätzen und auf neue zu übertragen. Weiterhin können sie Techniken und Protokolle einsetzen, um Sicherheitsziele zu erreichen, die mit den in Kryptographie I besprochenen Verfahren noch nicht möglich waren.
Personale Kompetenz:
Das analytische, konzentrierte und präzise Denken der Studierenden wird geschult. Durch die eigenständige Behandlung von Anwendungen, z.B. im Rahmen der Übungsaufgaben, wird ihr Abstraktionsvermögen weiterentwickelt und der Transfer des erlernten Stoffes auf verwandte Fragestellungen gefördert.
Recommended requirement:
Literature:
Es gibt keine formalen Voraussetzungen, aber folgende inhaltliche Vorkenntnisse werden empfohlen:
Grundkenntnisse in der Kryptographie, wie sie bspw. in der Vorlesung “Kryptographie I” erworben werden können.
CS 550 Algorithmik
Examination achievement:
Es gibt keine formalen Voraussetzungen, aber folgende inhaltliche Vorkenntnisse werden empfohlen:
Grundkenntnisse in der Kryptographie, wie sie bspw. in der Vorlesung “Kryptographie I” erworben werden können.
CS 550 Algorithmik
Instructor(s):
Frederik Armknecht
Description:
In der Vorlesung erfolgt eine kurze Zusammenstellung der wichtigsten kryptographischen Grundalgorithmen und der für die Vorlesung relevanten mathematischen, algorithmischen und informations- und komplexitätstheoretischen Grundlagen. Diese werden einerseits vertieft und andererseits erweitert. Behandelte Themen sind beispielsweise
  • moderne Techniken der Kryptanalyse und daraus ableitbare Designkriterien für kryptographische Verfahren
  • kryptographische Protokolle
  • Sicherheitsbeweise
Model Driven Development (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
Students will be familiar with the accepted best practices and technologies used in mainstream model-driven development as well as state-of-the-art modeling technologies emerging from research institutions.
Methodological competence:
Students will know how to apply modeling technologies in real-world projects.
Personal competence:
Students will have the capability to analyse, understand and model complex systems.
Recommended requirement:
Literature:
Advanced Software Engineering
Examination achievement:
Advanced Software Engineering
Instructor(s):
Colin Atkinson
Description:
The course focuses on the principles, practices and tools involved in advanced model-driven development. This includes established modelling standard languages (e. g. UML, ATL, OCL . . . ) and modelling infrastructures (e. g. MOF, EMF, etc. ) as well as leading edge, state-of-the-art modelling technologies (e. g. LML, PLM . . . ). Key topics addressed include –
  • Multi-level modeling
  • Meta-modeling
  • Ontology engineering versus model engineering
  • Model transformations
  • Domain specific language definition and use
  • Model creation and evolution best practices
  • Model-driven software development
  • Model checking and ontology validation
Relational Learning (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Instructor(s):
Heiner Stuckenschmidt
Semantic Web Technologies (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
The participants of this course learn about principles and applications of Semantic Web standards. They become familiar with their technical foundations such as representation and query languages, or logical inference. After taking this course, the students will be aware of the problems and benefits of semantic technologies in the context of tasks such as knowledge management, information search and data integration, and they will be capable of judging the applicability of these technologies for addressing practical challenges.
Methodological competence:
The participants learn how to design and implement Semantic Web applications. They are able to use standardized modeling languages for building knowledge representations, and to query these models by means of languages such as SPARQL.
Personal competence:
By jointly building a semantic web application, the students learn how to effectively work in teams. They improve upon their presentation skills by showing the outcomes of their projects to the other participants of the course.
Recommended requirement:
Literature:
Java programming skills
Examination achievement:
Java programming skills
Instructor(s):
Description:
  • Vision and Principles of the Semantic Web
  • Representation Languages (XML, RDF, RDF Schema, OWL)
  • Knowledge Modeling: Ontologies and Linked Data
  • Logical Reasoning in RDF and OWL
  • Commercial and Open Source Tools and Systems
Text Analytics (Lecture, 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 state-of-the-art principles and methods of Natural Language Processing, with a specific focus on the application of statistical methods to human language technologies.
Methodological competence:
Successful participants will be able to understand state-of-the-art methods for Natural Language Processing, as well as being able to select, apply and evaluate the most appropriate techniques for a variety of different practical and application-oriented scenarios.
Personal competence:
  • presentation skills;
  • team work skills.
Recommended requirement:
Literature:
Programming skills (Java or C++ preferred). Fundamental notions of linear algebra and probability theory.
Examination achievement:
Programming skills (Java or C++ preferred). Fundamental notions of linear algebra and probability theory.
Instructor(s):
Description:
In the digital age, techniques to automatically process textual content have become ubiquitous. Given the breakneck speed at which people produce and consume textual content online – e.g., on micro-blogging and other collaborative Web platforms like wikis, forums, etc. – there is an ever-increasing need for systems that automatically understand human language, answer natural language questions, translate text, and so on. This class will provide a complete introduction to state-of-the-art principles and methods of Natural Language Processing (NLP). The main focus will be on statistical techniques, and their application to a wide variety of problems. This is because statistics and NLP are nowadays highly intertwined, since many NLP problems can be formulated as problems of statistical inference, and statistical methods, in turn, represent de-facto the standard way  to solve many, if not the majority, of NLP problems. Covered topics will include:
 
  • Words
    • Language Modeling
    • Part-Of-Speech Tagging
  • Syntax
    • Statistical Parsing
  • Semantics and pragmatics
    • Computational Lexical Semantics
    • Computational Discourse
  • Applications
    • Topic Modeling
    • Information Extraction
    • Question Answering and Summarization
    • Statistical Alignment and Machine Translation
Coursework will include homework assignments and a final exam. Homework assignments are meant to introduce the students to the problems that will be covered in the final exam at the end of the course.
Web Data Integration (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
Students will be able to identify opportunities for employing Web data in business applications and will learn to select and apply appropriate techniques for integrating and cleansing Web data.
Methodological competence:
  • Participants will acquire knowledge of the data integration process as well as the techniques that are used in each phase of the process.
  • project organization skills

Personal competence:

  • presentation skills
  • team work skills.
Recommended requirement:
Literature:
Programming skills in Java
Examination achievement:
Programming skills in Java
Instructor(s):
Christian Bizer
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
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Business Mathematics (Bachelor)

MAA 510 Introduction of partial differential equations (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Martin Schmidt
MAB 504 Mathematics and Information (Lecture, English)
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Fachkompetenz:
Quantisierung von Information und inhaltliche Interpretation der entsprechenden Maße (MK1, MO2)
Verständnis für Möglichkeiten und Grenzen log-optimaler Anlagestrategien (MK2, MF1, MF2)
Verständnis für die Rolle der Linearen Algebra in der Informationssuche und der Klassifikation von Information (MK1, MK2, MF1, MF2)
Methodenkompetenz:
Umgang mit gängigen Informationsmaßen (MF2)
Datenkompression mit Huffman-Bäumen und mit Transformationen (MO2)
Berechnung log-optimaler und universeller Portfolios (MK2, MF1, MF2)
Berechnung von PageRank und verwandten Rängen (MK1, MK2, MF1, MF2)
Latente semantische Analyse via Singulärwertzerlegung (MK2, MF2)
Personale Kompetenz:
Fähigkeit, intuitiv gegebene Begriffe wie Information, optimale sichere Anlagestrategie, Wichtigkeit oder Ähnlichkeit von Dokumenten und Webseiten durch verschiedene Ansätze mathematisch zu modellieren und die Vor- und Nachteile der verschiedenen Möglichkeiten abzuschätzen (MK2, MF2, MO2, MO4)
Recommended requirement:
Literature:
Analysis I und II, Lineare Algebra I und IIa, Einführung in die Wahrscheinlichkeitstheorie
Examination achievement:
Analysis I und II, Lineare Algebra I und IIa, Einführung in die Wahrscheinlichkeitstheorie
Instructor(s):
Description:
Shannons Entropie und abgeleitete Informationsmaße
Entropie und Datenkompression
Die Wettstrategie von Kelly
Log-optimale Portfolios
Universelle Portfolios
Vektorraummethoden in der Informationssuche
Matrixzerlegungen und latente semantische Analyse
PageRank und verwandte Verfahren
MAB 508 Algebraic Statistics (Lecture, English)
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Instructor(s):
MAC 524 Asymptotic Analysis (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
2
Recommended requirement:
Literature:
Analysis I, II, Linear algebra I, Differential equations
Examination achievement:
Analysis I, II, Linear algebra I, Differential equations
Instructor(s):
Georgios Psaradakis
Description:
Asymptotic analysis is to describe the behavior of a certain function near its limit. This method is widely used in many scientific fields, such as computer science and physics. In this course, we will focus on the topic of asymptotic approximations. We will start with the fundamental ideas underlying asymptotic approximations, and then we will demonstrate how to use this method to find approximate solutions for problems, including algebraic equations, ordinary differential equations and even partial differential equations arising from physical water waves, sound propagation, and aerodynamics of airplanes. Furthermore, we will also discuss how to use the matched asymptotic expansions to analyze problems with layers, and examine the stability.
Seminar Linear Algebra in Search Engines (Seminar, English)
Lecture type:
Seminar
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Optimal Control of ODEs and DAEs (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Schillings
Optimal Control of ODEs and DAEs (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Optimal Control of ODEs and DAEs (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Schillings
Strategy and Games in Continuous Systems (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Instructor(s):
Simone Göttlich
Theory of conservation laws (Lecture, English)
Lecture type:
Lecture
ECTS:
5.0
Course suitable for:
Language of instruction:
English
Instructor(s):
Simone Göttlich
Uncertainty Quantification (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Schillings
Uncertainty Quantification (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Schillings

Business Mathematics (Master)

MAA 510 Introduction of partial differential equations (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Martin Schmidt
MAB 504 Mathematics and Information (Lecture, English)
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Fachkompetenz:
Quantisierung von Information und inhaltliche Interpretation der entsprechenden Maße (MK1, MO2)
Verständnis für Möglichkeiten und Grenzen log-optimaler Anlagestrategien (MK2, MF1, MF2)
Verständnis für die Rolle der Linearen Algebra in der Informationssuche und der Klassifikation von Information (MK1, MK2, MF1, MF2)
Methodenkompetenz:
Umgang mit gängigen Informationsmaßen (MF2)
Datenkompression mit Huffman-Bäumen und mit Transformationen (MO2)
Berechnung log-optimaler und universeller Portfolios (MK2, MF1, MF2)
Berechnung von PageRank und verwandten Rängen (MK1, MK2, MF1, MF2)
Latente semantische Analyse via Singulärwertzerlegung (MK2, MF2)
Personale Kompetenz:
Fähigkeit, intuitiv gegebene Begriffe wie Information, optimale sichere Anlagestrategie, Wichtigkeit oder Ähnlichkeit von Dokumenten und Webseiten durch verschiedene Ansätze mathematisch zu modellieren und die Vor- und Nachteile der verschiedenen Möglichkeiten abzuschätzen (MK2, MF2, MO2, MO4)
Recommended requirement:
Literature:
Analysis I und II, Lineare Algebra I und IIa, Einführung in die Wahrscheinlichkeitstheorie
Examination achievement:
Analysis I und II, Lineare Algebra I und IIa, Einführung in die Wahrscheinlichkeitstheorie
Instructor(s):
Description:
Shannons Entropie und abgeleitete Informationsmaße
Entropie und Datenkompression
Die Wettstrategie von Kelly
Log-optimale Portfolios
Universelle Portfolios
Vektorraummethoden in der Informationssuche
Matrixzerlegungen und latente semantische Analyse
PageRank und verwandte Verfahren
MAB 508 Algebraic Statistics (Lecture, English)
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Instructor(s):
MAC 524 Asymptotic Analysis (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
2
Recommended requirement:
Literature:
Analysis I, II, Linear algebra I, Differential equations
Examination achievement:
Analysis I, II, Linear algebra I, Differential equations
Instructor(s):
Georgios Psaradakis
Description:
Asymptotic analysis is to describe the behavior of a certain function near its limit. This method is widely used in many scientific fields, such as computer science and physics. In this course, we will focus on the topic of asymptotic approximations. We will start with the fundamental ideas underlying asymptotic approximations, and then we will demonstrate how to use this method to find approximate solutions for problems, including algebraic equations, ordinary differential equations and even partial differential equations arising from physical water waves, sound propagation, and aerodynamics of airplanes. Furthermore, we will also discuss how to use the matched asymptotic expansions to analyze problems with layers, and examine the stability.
SEM 511 Kinetic Models (Seminar, English)
Lecture type:
Seminar
ECTS:
Course suitable for:
Language of instruction:
English
Hours per week:
2
Instructor(s):
Li Chen
Seminar Linear Algebra in Search Engines (Seminar, English)
Lecture type:
Seminar
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
MAC 508 Computational SDEs (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Fachkompetenz: Die Studierenden haben die grundlegenden Fragestellungen  und wichtigsten Methoden der Numerik stochastischer Differentialgleichungen erlernt, insbesondere die    Unterschiede zwischen den verschiedenen Approximationsbegriffen, das Euler- und Milsteinverfahren  sowie Multi-level Monte-Carlo-Verfahren (MK1,M02).
Methodenkompetenz: Die Studierenden können nach Besuch des Moduls gegebene numerische Probleme für stochastische Differentialgleichungen klassifizieren und zur Bearbeitung geeignete Verfahren auswählen bzw. konstruieren (MF1,MF2,MO3).
Personale Kompetenz: Teamarbeit
Recommended requirement:
Literature:
Numerik, Wahrscheinlichkeitstheorie I, Stochastische Simulation
Examination achievement:
Numerik, Wahrscheinlichkeitstheorie I, Stochastische Simulation
Instructor(s):
Peter Parczewski
Description:
Theoretische Grundlagen: stochastische Prozesse; stochastische Integration und stochastische Differentialgleichungen.
Numerik: Simulation von Gaußprozessen; Fehlerbegriffe; Klassische Approximationsverfahren; Cameron-Clark Theorem; Quadratur von SDGLn; Anwendungen in Technik und Finanzmathematik
Optimal Control of ODEs and DAEs (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Schillings
Optimal Control of ODEs and DAEs (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Optimal Control of ODEs and DAEs (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Schillings
Strategy and Games in Continuous Systems (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Instructor(s):
Simone Göttlich
Theory of conservation laws (Lecture, English)
Lecture type:
Lecture
ECTS:
5.0
Course suitable for:
Language of instruction:
English
Instructor(s):
Simone Göttlich
Uncertainty Quantification (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Schillings
Uncertainty Quantification (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Schillings