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A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks …
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.Reinforcement …
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.Gaussian …
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research …
New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process. In many practical …
A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this book, Brendan Frey uses graphical models …
A comprehensive introduction to Support Vector Machines and related kernel methods.In the 1990s, a new type of learning algorithm was developed, based on results from statistical …
A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with …