Monte-Carlo Tree Search and Billiards - VideoLectures

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Why do we bother?Machine Learning tackles a wide range ofproblemsFor some of them, data is expensiveLabel = $$A supervised learning problem

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Why do we bother?Machine Learning tackles a wide range ofproblemsFor some of them, data is expensiveLabel = $$A supervised learning problem

A major Machine Learning goalReduce sample complexitywhile keeping generalization error lowMotivating application: numerical engineering=> Learn simplified models with only ~ 100 examples

Page 1: European Conference in Machine Lear
Page 5 and 6: How AL typically worksFind a way to
Page 7 and 8: Train the Active Learning PlayerIns
Page 9 and 10: UCT: Exploration Vs ExploitationUCB
Page 11 and 12: The Baal AlgorithmValue function fo
Page 13 and 14: Baal: draw surrogate hypothesesBill
Page 15 and 16: Some results - 2Combining with ALcr
Page 17: Thanks for listening

Monte-Carlo Tree Search and Billiards - VideoLectures

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