About
The school provides tutorials and practical sessions on basic and advanced topics of machine learning by leading researchers in the field. The summer school is intended for students, young researchers and industry practitioners with an interest in machine learning and a strong mathematical background.
The school addresses the following topics: Learning Theory, Bayesian inference, Monte Carlo Methods, Sparse Methods, Reinforcement Learning, Robot Learning, Boosting, Kernel Methods, Bayesian Nonparametrics, Convex Optimization and Graphical Models.
Detailed information can be found at the summer school homepage.
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Uploaded videos:
Invited Talks
Low-rank modeling
Oct 12, 2011
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24603 Views
Early language bootstrapping
Oct 12, 2011
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5703 Views
Tutorials
Kernel Methods
Oct 12, 2011
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16054 Views
Monte Carlo Methods
Oct 12, 2011
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18087 Views
Bayesian Inference
Oct 12, 2011
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27668 Views
Bayesian Nonparametrics
Oct 12, 2011
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35634 Views
Sparse Methods for Under-determined Inverse Problems
Oct 12, 2011
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8614 Views
Convex Optimization
Oct 12, 2011
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21358 Views
Learning Theory: statistical and game-theoretic approaches
Oct 12, 2011
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8077 Views
Graphical Models and message-passing algorithms
Oct 12, 2011
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29034 Views