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Instructors: Prof. Ph. D. Jan Peters
Event type:
Integrated Course
Org-unit: Dept. 20 - Computer Science
Displayed in timetable as:
Machine Learning
Subject:
Crediting for:
Hours per week:
4
Language of instruction:
Englisch
Min. | Max. participants:
- | -
Course Contents:
Objectives:
Introduction and overview of statistical appraoches of machine learning.
Course Content:
The lecture gives a systematic introduction to statistical methods for machine learning. The following list of topics gives (as an example) an overview of topics: Probability Distributions, Linear Models for Regression and Classification, Kernel Methods, Gaphical Models, Mixture Models and EM, Approximate Inference, Continuous Latent Variables, Hidden Markov Models
Diploma Supplement:
Statistical Methods for Machine Learning, Bayes Decision Theory, Density Estimation, Linear Models for Classification and Regression, Statistical Learning Theory, Kernel Methods and Support Vector Machines, Graphical Models for Learning, Approximate Inference
Literature:
Main Book: - C.M. Bishop, Pattern Recognition and Machine Learning (2006), Springer
Suplementary Reading: - R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification (2nd ed. 2001)m Willey-Interscience
- T.M. Mitchell, Machine Learning (1997), McGraw-Hill
- T. Hastie, R. Tibshirani, and J. Friedman (2003), The Elements of Statistical Learning, Springer
Preconditions:
Prerequisites: statistisches und mathematisches Grundwissen, lineare Algebra, algorithmische Grundlagen
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