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.
Videos
Invited Talks

Low-rank modeling
Oct 12, 2011
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24637 views

Early language bootstrapping
Oct 12, 2011
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5717 views
Tutorials

Bayesian Nonparametrics
Oct 12, 2011
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35752 views

Bayesian Inference
Oct 12, 2011
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27809 views

Learning Theory: statistical and game-theoretic approaches
Oct 12, 2011
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8122 views

Kernel Methods
Oct 12, 2011
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16098 views

Sparse Methods for Under-determined Inverse Problems
Oct 12, 2011
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8669 views

Monte Carlo Methods
Oct 12, 2011
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18132 views

Graphical Models and message-passing algorithms
Oct 12, 2011
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29115 views

Convex Optimization
Oct 12, 2011
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21438 views