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:

• Exercise1 (Solution_exercise_1-1b)
• 2012Zaitlen_Heritability
• GP_tutorial

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.