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

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

Updated 2018

 

Copied from: https://medium.com/datadriveninvestor/a-curated-list-of-artificial-intelligence-ai-courses-books-video-lectures-and-papers-d2f584ca14f8

 

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

Contributions most welcome.

Courses

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.
  • Deep Learning and the Game of Go — Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex human-flavored reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you’ll use Python to build a bot and then teach it the rules of the game.
  • Deep Learning for Search — Deep Learning for Search teaches you how to leverage neural networks, NLP, and deep learning techniques to improve search performance.
  • Deep Learning with PyTorch — PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you — and your deep learning skills — become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun.
  • Deep Reinforcement Learning in Action — Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.
  • Grokking Deep Reinforcement Learning — Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching.

Programming

Philosophy

  • Super Intelligence — 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 applies that knowledge to create vastly intelligent machines.
  • 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.
  • Life 3.0: Being Human in the Age of Artificial Intelligence — Max Tegmark, professor of Physics at MIT, discusses how Artificial Intelligence may affect crime, war, justice, jobs, society and our very sense of being human both in the near and far future.

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
  • FARGonautica — Source code of Douglas Hosftadter’s Fluid Concepts and Creative Analogies Ph.D. projects.

Videos

Organizations

Journals

Competitions

Movies

Misc

 

posted @ 2018-10-07 19:40  AHU-WangXiao  阅读(441)  评论(0)  编辑  收藏  举报