Algorithms for Reinforcement Learning

Morgan & Claypool Publishers

In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. ISBN 9781608454921, 103 pages.


Introduction to Semi-Supervised Learning

Morgan & Claypool Publishers

In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. ISBN 9781598295474, 130 pages.


Metric Learning

Morgan & Claypool Publishers

This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. ISBN 9781627053655, 151 pages.


When reinforcement learning should not be used?

@machinelearnbot

While reinforcement learning has achieved many successes, there are situations when it use is problematic. We describe the issues and how to work around them.