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Free Artificial Intelligence books

Reinforcement Learning: An Introduction
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 algorithms of reinforcement learning.
Introduction to Machine Learning
This free pdf book by Nils J. Nilsson includes the following chapters: Preliminaries, Boolean Functions, Using Version Spaces for Learning, Neural Networks, Statistical Learning, Decision Trees, Inductive Logic Programming, Computational Learning Theory, Unsupervised Learning, Temporal-Difference Learning, Delayed-Reinforcement Learning and Explanation-Based Learning.  
How Are We To Know?
This book makes the following points: 1) in analogy with robots, we humans know by the models we make of reality, 2) these models are always provisional and sometimes unreliable, 3) it is especially important to examine thoroughly those models upon which we base actions, and 4) the scientific method provides an excellent guide for such examination.  
Planning Algorithms
Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. This free book can be downloaded in pdf and html formats.
An Introduction to Neural Networks
Although mathematical ideas underpin the study of neural networks, this book presents the fundamentals without the full mathematical apparatus. The author tackles virtually all aspects of the field, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods; associative memory and Hopfield nets; and self-organization and feature maps.



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