Machine Learning School, Cambridge 2009

Old but very explanatory:

Topics titles

1) Introduction to Bayesian Inference

2) Graphical Models

3) Markov Chains and Monte Carlo

4) Information Theory

5) Kernel Methods

6) Approximate Inference

7) Topic Models

8) Gaussian Processes

9) Convex Optimization

10) Learning Theory

11) Computer Vision

12) Nonparametric Bayesian Models

13) Machine Learning and Cognitive Science

14) Reinforcement Learning

15) Foundations of Nonparametric Bayesian Methods

16) Deep Belief Networks

17) Particle Filters

18) Causality

19) Information Retrieval

20) Bayesian or Frequentist? Which Are You?


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