Artificial Intelligence Data Encyclopedia

online tutorial

MIT Artificial Intelligence Video Tutorials – MIT Artificial Intelligence Course
Introduction to Artificial Intelligence - Learning the basics of artificial intelligence. Courses by Peter Norvig
EdX Artificial Intelligence – This course teaches fundamental concepts and techniques for the design of artificial intelligence computer systems.
Planning in AI – Planning is one of the fundamental parts of an AI system. In this course, you will learn the basic algorithms needed to make a robot perform a series of actions.
Robotic Artificial Intelligence - This course will teach you the basic methods of implementing artificial intelligence, including: probabilistic calculation, planning and searching, localization, tracking and control, all around robotic design.
Machine Learning – Basic machine learning algorithms with and without guidance
Neural Networks in Machine Learning – Algorithms and Practical Experience on Intelligent Neural Networks
斯坦福统计学习 -Introductory course on machine learning focusing on: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines.

Artificial Intelligence: A Modern Approach Stuart Russell & Peter Norvig
The Cambridge Handbook of Artificial Intelligence – for the non-specialist, this book covers basic principles, major theories and major areas of research, as well as related topics such as artificial life.
The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind – In this mind-bending book, the tech pioneer continues his creative research, presenting a new The incredible workings of the human brain.
Artificial Intelligence: A New Synthesis - Starting with particle reactions, Nilsson gradually shows us the most important and newest concepts in artificial intelligence.

Prolog Programming for Artificial Intelligence - This best-selling book on Prolog and artificial intelligence focuses on the basic mechanics of programming in the Proglog language to solve interesting artificial intelligence problems.
AI Algorithms, Data Structures and Idioms in Prolog, Lisp and Java – PDF here

Superintelligence - The book asks: What if machines surpass humans in intelligence. Very good book.
Our Final Invention: Artificial Intelligence and the End of the Human Era – Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?
How to Create a Mind: The Secret of Human Thought Revealed - Ray Kurzweil - Technical Lead at Google - shows us how to reverse engineer how the brain works and then use that knowledge to create artificially intelligent machines .

Foundations of computational agents - This book was published by Cambridge University Press in 2010.
The Quest for Artificial Intelligence – This book reviews the history of artificial intelligence, from the early 18th century dreams of our ancestors to the many successful artificial intelligence technologies we have today.

AIMA Lisp Source Code - Common Lisp source code from the book "Artificial Intelligence A Modern Approach".

The Unreasonable Effectiveness of Deep Learning - Dr. Yann LeCun - Director of AI Research at Facebook - gives us an in-depth look at artificial intelligence neural networks and their application to machine learning.

Deep Learning. Methods and Applications is a free reading from Microsoft Research.
Neural Networks and Deep Learning – Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning
Machine Learning: A Probabilistic Perspective - This little book gives a thorough introduction to machine learning
Deep Learning – Yoshua Bengio, Ian Goodfellow and Aaron Courville put together this currently free (and draft version) book on deep learning. The book is kept up-to-date and covers a wide range of topics in depth (up to and including sequence-to-sequence learning).