392245 Applied Modelling (S) (SoSe 2016)

This is a 3 CP course as part of the 'Ergänzungsmodul Informatik' 39-Inf-EGMI. However, the course is open to members of all faculties. The course will be entirely in English.

The course aims at providing students with a toolbox of modelling frameworks to express problems in a way that yields natural solutions. In other words: it is a course about looking at problems from the right angle, such that solutions become obvious.

In more detail, the course will introduce the following modelling frameworks:

• Graph Theory (applications e.g. in object-oriented programming, network analysis and the semantic web)
• Formal Languages (applications e.g. in compiler building and bioinformatics)
• (Convex) Optimization (applications e.g. in machine, robotics learning and economy)
• Dynamical Systems (applications e.g. in control theory, physics)
• Probability Theory & Bayesian Reasoning (applications e.g. in machine learning and robotics)
• Self-Learning Systems/Neural networks (applications e.g. in pattern recognition and language processing)

The seminar will provide pointers for further reading or other courses here in Bielefeld to get a more in-depth picture of the topics introduced. Students will also get the chance to exercise their newly-won modelling skills on a problem of their choice and present their model to the other seminar members in a 5-10 page paper as well as a 30-minute oral presentation.

Requirements for participation, required level

Prior knowledge is not strictly required. However, mathematical models are the topic of this seminar. Therefore, it is recommended to have some prior exposition to university grade math.

Bibliography

This is a list of pointers for further reading. Keep in mind that the seminar will cover only a small subset of the topics presented in these books and that the books do not cover everything presented in the seminar. It is not required to read these books for the seminar.

• Neil Gershenfeld (1999): The Nature of Mathematical Modeling, Cambridge University Press
• Zbigniew Michalewicz, David Fogel (2000): How to Solve It: Modern Heuristics, Springer
• Stephen Boyd, Lieven Vandenberghe (2009): Convex Optimization, Cambridge University Press
• David Barber (2010): Bayesian Reasoning and Machine Learning, Cambridge University Press
• Christopher Bishop (2006): Pattern Recognition and Machine Learning, Springer