Machine Learning Summer School (MLSS ), Bordeaux 2011
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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The Machine Learning Summer School (MLSS) in Bordeaux 2011 offered a comprehensive program covering fundamental and advanced machine learning topics. Participants, including students, researchers, and industry professionals with a solid mathematical foundation, benefitted from tutorials and practical sessions led by prominent researchers in the field. The curriculum spanned diverse areas such as Learning Theory, Bayesian inference, Reinforcement Learning, and more. The event aimed to foster knowledge exchange and skill development, contributing to the growth of the machine learning community. Visit the summer school homepage for further details.
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