Literature:
- 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)
- Course Scriptum Computational Intelligence (required
background) (PDF)
- Review of basic concepts of probability theory
(PDF)