The Future of Machine Intelligence
by David Beyer
Publisher: O'Reilly Media 2016
Number of pages: 78
The series of interviews in this exclusive report unpack concepts and innovations that represent the frontiers of ever-smarter machines. You’ll get a rare glimpse into this exciting field through the eyes of some of its leading minds.
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by Richard S. Sutton, Andrew G. Barto - The MIT Press
The book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. It covers the history and the most recent developments and applications. The only necessary mathematical background are concepts of probability.
by Zdravko Markov - Central Connecticut State University
Contents: Introduction; Concept learning; Languages for learning; Version space learning; Induction of Decision trees; Covering strategies; Searching the generalization / specialization graph; Inductive Logic Progrogramming; Unsupervised Learning ...
by Robert E. Schapire, Yoav Freund - The MIT Press
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
by Jan-Willem van de Meent, et al. - arXiv.org
This text is designed to be a graduate-level introduction to probabilistic programming. It provides a thorough background for anyone wishing to use a probabilistic programming system, and introduces the techniques needed to build these systems.