Awesome Artificial Intelligence (AI) Resources

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


Contents

  1. Courses
  2. Books
  3. Programming
  4. Philosophy
  5. Free Content
  6. Code
  7. Videos
  8. Learning
  9. Organizations
  10. Journals
  11. Competitions
  12. Movies
  13. Misc

Courses

  • 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 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.
  • 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.
  • Knowledge Based Artificial Intelligence — Georgia Tech’s course on Artificial Intelligence focussing on Symbolic AI.

Books

  • Artificial Intelligence: A Modern Approach — Stuart Russell & Peter Norvig
  • Also consider browsing the list of recommended reading, divided by each chapter in “Artificial Intelligence: A Modern Approach”.
  • Paradigms Of Artificial Intelligence Programming: Case Studies in Common Lisp — Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems
  • 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.
  • 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
  • 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.
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction — Hastie and Tibshirani cover a broad range of topics, from supervised learning (prediction) to unsupervised learning including neural networks, support vector machines, classification trees and boosting — -the first comprehensive treatment of this topic in any book.

Programming

Philosophy

  • Super Intelligence — Superintelligence asks the questions: What happens when machines surpass humans in general intelligence. A really great book.




  • 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.


Free Content

Code

  • AIMACode — Source code for “Artificial Intelligence: A Modern Approach” in Common Lisp, Java, Python. More to come.
  • FANN — Fast Artificial Neural Network Library, native for C

Videos

Learning


Organizations

Journals
















Competitions

Movies





Misc

  • Open Cognition Project — We’re undertaking a serious effort to build a thinking machine
  • AITopics — Large aggregation of AI resources
  • AIResources — Directory of open source software and open access data for the AI research community


Source: https://github.com/owainlewis/awesome-artificial-intelligence

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