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The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intellig

This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Agents and Artificial Intelligence, ICAART 2012

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, opti

Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms.

Machine Learning Proceedings 1992