Theoretical Concepts of Machine Learning (2VO)

Course no.: 365.041
Lecturer: Ulrich Bodenhofer
Start: Oct 7, 2009
Time: Wed 1:45-3:15pm/4:15pm
Changes: No lectures on Nov 4 and 18;
additional lectures on Nov 12 and Nov 26 (both 1:45pm-3:15pm in KG712)
No lecture on Jan 13;
additional lecture on Jan 14 (1:45pm-4:15pm in KG712)
Location: KG 712
Mode: VO, 2-3h, weekly
Registration: KUSSS
Written exam: Thu, Feb 11, 2010, 3:30-4:30pm (register via KUSSS)
Oral exams: upon individual appointment


Machine learning methods, i.e. methods that infer models/relationships by learning from data, are still gaining importance in various fields, such as, process modeling, speech and image processing, bioinformatics, and so forth. Their ability to cope with tasks for which no analytical model is available ideally complements classical approaches. One has to acknowledge, however, that machine learning methods also bear great risks if they are applied inappropriately. The given lecture provides a look behind the curtain of machine learning. The goal is to make students acquainted with the basic concepts and methods to analyze, evaluate and understand models created by machine learning. In the sequel, we will also have a closer look at support vector machines and neural networks from this foundational perspective.


Necessary Background

Parts of the lecture will be quite mathematical, so a profound background in calculus, probability and statistics is necessary. This should not be a problem for graduate students of mathematics, computer science, physics, mechatronics, and statistics. Prior knowledge of machine learning (e.g. attendance of Prof. Widmer's lecture "Machine Learning and Pattern Classification") is surely helpful, but not an absolute pre-requisite. Students of bioinformatics should take into account that there is a significant overlap with the lecture "Bioinformatics II: Theoretical Bioinformatics and Machine Learning".

Course Material


© 2009 Ulrich Bodenhofer
This material, no matter whether in printed or electronic form, may be used for personal and educational use only. Any reproduction of this material, no matter whether as a whole or in parts, no matter whether in printed or in electronic form, requires explicit prior acceptance of the author.

Software demos

Notes for further reading

Books recommended for further reading