Course info

Lehrinhalte
- Statistical Methods for Machine Learning
- Refreshers on Statistics, Optimization and Linear Algebra
- Bayes Decision Theory
- Probability Density Estimation
- Non-Parametric Models
- Mixture Models and EM-Algorithms
- Linear Models for Classification and Regression
- Statistical Learning Theory
- Kernel Methods for Classification and Regression

Literature
1. C.M. Bishop, Pattern Recognition and Machine Learning (2006), Springer
2. K.P. Murphy, Machine Learning: a Probabilistic Perspective (expected 2012), MIT Press
3. D. Barber, Bayesian Reasoning and Machine Learning (2012), Cambridge University Press
4. T. Hastie, R. Tibshirani, and J. Friedman (2003), The Elements of Statistical Learning, Springer Verlag
5. D. MacKay, Information Theory, Inference, and Learning Algorithms (2003), Cambridge University Press
6. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification (2nd ed. 2001), Willey-Interscience
7. T.M. Mitchell, Machine Learning (1997), McGraw-Hill

Bemerkung Webportal
[b]Area:[/b] Human Computer Systems

[b]Registration:[/b]
Mittwoch, 15.04.09 09.50 - 11.20 S3 05/073

[b]Preliminary discussion:[/b]
Mittwoch, 15.04.09 09.50 - 11.20 S3 05/073