- week1_probability theory
Exercise sheets and reading material:
Useful material and literature:
- Linear mixed models for genome-wide association studies, (by Christoph Lippert), PhD thesis
- Gaussian identities (by Sam Roweis)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman. Springer, Second Edition, 2009. full online version available (recommended: read introductory chapter)
- Bayesian Reasoning and Machine Learning (by David Barber), Cambridge University Press, 2012.
- 26 page linear algebra and matrix calculus reference. It’s used for the ML class in Stanford. link
More advanced material:
- The matrix cookbook (useful for advanced matrix calculations)
- Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Chris Williams, the MIT Press, 2006, online version.
- Information Theory, Inference and Learning Algorithms (by David MacKay), Cambridge University Press, 2003.