Course Scriptum Computational Intelligence (required background) (PDF)
Sutton/Barto: Reinforcement Learning: An Introduction, MIT Press
Review of basic concepts of probability theory (PDF)
C. Bishop: Pattern Recognition and Machine Learning, Springer Verlag, 2006. In der Lehrbuchsammlung vorhanden (20 Stück) (PDF)
D. Barber: Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012. In der Lehrbuchsammlung vorhanden (10 Stück) (PDF); (Errata-PDF)
K. P. Murphy: Machine Learning: A Probabilistic Perspective (Adaptive Computation and machine Learning series). MIT Press, 2012. In der Lehrbuchsammlung vorhanden (10 Stück)
D. Kollar and N. Friedman: Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009. (PDF)
T. Hastie, R. Tibshirani and J. Friedman: The elements of statistical learning. 2nd edition. New York: Springer, 2001. (PDF)
Andrieu et al. (2003) An Introduction to MCMC for Machine Learning
Neal (1993) Probabilistic Inference Using Markov Chain Monte Carlo Methods
Kemp, Perfors and Tenenbaum (2007) Learning overhypotheses with hierarchical Bayesian models
Video lectures, Cognitive Science and Machine Learning Summer School 2010 - Sardinia