EdX Artificial Intelligence - The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems
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
Deep Learning - An Introductory course to the world of Deep Learning.
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.
Reinforcement Learning: An Introduction - This introductory textbook on reinforcement learning is targeted toward engineers and scientists in artificial intelligence, operations research, neural networks, and control systems, and we hope it will also be of interest to psychologists and neuroscientists.
On Intelligence - Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines. Also audio version available from audible.com
How To Create A Mind - Kurzweil discusses how the brain works, how the mind emerges, brain-computer interfaces, and the implications of vastly increasing the powers of our intelligence to address the world’s problems
Deep Learning - Goodfellow, Bengio and Courville's introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
Minds, Brains, And Programs - The 1980 paper by philospher John Searle that contains the famous 'Chinese Room' thought experiment. Probably the most famous attack on the notion of a Strong AI possessing a 'mind' or a 'consciousness', and interesting reading for those interested in the intersection of AI and philosophy of mind.
Gödel, Escher, Bach: An Eternal Golden Braid - Written by Douglas Hofstadter and taglined "a metaphorical fugue on minds and machines in the spirit of Lewis Carroll", this wonderful journey into the the fundamental concepts of mathematics,symmetry and intelligence won a Pulitzer Price for Non-Fiction in 1979. A major theme throughout is the emergence of meaning from seemingly 'meaningless' elements, like 1's and 0's, arranged in special patterns.
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.
Computers and Thought: A practical Introduction to Artificial Intelligence - The book covers computer simulation of human activities, such as problem solving and natural language understanding; computer vision; AI tools and techniques; an introduction to AI programming; symbolic and neural network models of cognition; the nature of mind and intelligence; and the social implications of AI and cognitive science.
Society of Mind - Marvin Minsky's seminal work on how our mind works. Lot of Symbolic AI concepts have been derived from this basis.
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
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).