Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algor...(展开全部)

Only when one has lost all curiosity about the future has one reached the age to write an autobiography'. Waugh begins his story with heredity, writing of the energetic, literary and sometimes eccentric men and women who, unknown to themselves, contributed to his genius. Save for a few pale shadows, his childhood was warm, bright and serene. The Hampstead and Lancing schooldays...(展开全部)

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (inc...(展开全部)

Written in an easily accessible style, this book provides the ideal blend of theory and practical, applicable knowledge. It covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equa...(展开全部)

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, a...(展开全部)

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (inc...(展开全部)

玛克辛·格林（MaxineGreene，1917.12.23—2014.5.29），美国当代*伟大的教育思想家之一，社会活动家，世界教育哲学界的代表人物，1965年起任职美国哥伦比亚大学教师学院教授。玛克辛·格林是美国教育研究协会的**一位女性主席，也曾担任美国教育研究联合会和教育哲学协会主席。她在1976—2012年担任美国林肯艺术中心长驻哲学家。

Josh Waitzkin knows what it means to be at the top of his game. A public figure since winning his first National Chess Championship at the age of nine, Waitzkin was catapulted into a media whirlwind as a teenager when his father's book Searching for Bobby Fischer was made into a major motion picture. After dominating the scholastic chess world for ten years, Waitzkin expanded...(展开全部)

Authors introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. DLC: Machine learning.