A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

Contributions most welcome.

• MIT Artifical Intelligence Videos - MIT AI Course
• Intro to Artificial Intelligence - Learn the Fundamentals of AI. Course run by Peter Norvig
• EdX Artificial Intelligence - The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems
• Artificial Intelligence Planning - Planning is a fundamental part of intelligent systems. In this course, for example, you will learn the basic algorithms that are used in robots to deliberate over a course of actions to take
• Artificial Intelligence for Robotics - This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics
• Machine Learning - Basic machine learning algorithms for supervised and unsupervised learning
• Neural Networks for Machine Learning - Algorithmic and practical tricks for artifical neural networks.
• Stanford Statistical Learning - 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 - Written for non-specialists, it covers the discipline's foundations, major theories, and principal research areas, plus related topics such as artificial life
• The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind - In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work
• Artificial Intelligence: A New Synthesis - Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI

• Prolog Programming for Artificial Intelligence - This best-selling guide to Prolog and Artificial Intelligence concentrates on the art of using the basic mechanisms of Prolog to solve interesting AI problems.
• AI Algorithms, Data Structures and Idioms in Prolog, Lisp and Java - PDF here

• Superintelligence - Superintelligence asks the questions: What happens when machines surpass humans in general intelligence. A really great 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, director of engineering at Google, explored the process of reverse-engineering the brain to understand precisely how it works, then applys that knowledge to create vastly intelligent machines.

• Foundations of computational agents - This book is published by Cambridge University Press, 2010
• The Quest for Artificial Intelligence - This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers.

• AIMA Lisp Source Code - Common Lisp source code for "Artificial Intelligence A Modern Approach"

• The Unreasonable Effectiveness of Deep Learning - The Director of Facebook's AI Research, Dr. Yann LeCun gives a talk on deep convolutional neural networks and their applications to machine learning and computer vision

• Deep Learning. Methods and Applications Free book 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 textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach

• Open Congition Project - We're undertaking a serious effort to build a thinking machine