**Lecture slides:**

- week1_probability theory
- week2_linear_regression_gwas_gaussians
- week3_statistical_testing_introduction
- week4_regularized_regression_Bayesian_regression
- 5_LinearMixedModels
- 6_GaussianProcesses
- PCA
- 8_GPLVM
- clustering_mixture_Gaussians

**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.