The best online AI instructional material that I know of. Please tell me if you know any other good stuff! This is an important page, because if you can’t get started, you’re nowhere.
| Jay : game learning : tutorials |
MOOC List - Artificial Intelligence
A list of Massive Open Online Courses in AI subjects. The research paper MOOCs and the AI-Stanford like Courses: Two Successful and Distinct Course Formats for Massive Open Online Courses by C. Osvaldo Rodriguez sees two different varieties of MOOC.
Artificial Intelligence | Free Online Courses
Another list of MOOCs, not as pretty but longer, with courses at all levels.
MIT 6.034 - Artificial Intelligence
The MIT Open Courseware course material for artificial intelligence as taught at MIT. Motivated students can work through the material on their own.
Machine Learning -
Chris Thornton
A short course introducing various basic machine learning methods. This is easier than the MIT course and a good way to learn some basic techniques, but it only covers machine learning and is short on overview and motivation.
Genetic Algorithms Overview
The simple overview of the most common variety of genetic algorithm, a good starting point. Part of the Genetic Algorithms Warehouse, which is pretty good (though not as good as its name).
A Genetic Algorithm Tutorial (1994)
Darrell Whitley
This postscript paper goes over the basics of the theory and explains variants with little to say about practice.
The Genetic Programming Tutorial - Jaime Fernandez
In genetic programming, the solutions you evolve are themselves
computer programs. That’s as much power as you can get.
Neural Networks in Plain English
A short practical introduction for absolute beginners, with C++ code.
Artificial Neural Networks - Wikipedia
The encyclopedia article is not well aimed at beginners (as of February 2016), but it does point out the basics and may be an OK base to start from. It’s a good source on history and background.
Technically neural networks, but the buzzword has a life of its own.
A Deep Learning Tutorial: From Perceptrons to Deep Networks
Explanations starting from zero and yet going reasonably far. Does not emphasize coding.
Caffe Tutorial
Caffe is a deep learning software package; there are several others that you might prefer instead. This tutorial is specific to the package, but seems good if your first instinct is to dig into the code rather than into the theory.
Deep Learning Tutorials
From Deeplearning.net. More information, deeper knowledge, fewer explanations. Maybe a good second source once you’ve gotten underway.
Reinforcement learning: a survey (1996) (CiteSeer link)
Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore
The review paper of reinforcement learning. It will help if you already know something about machine learning before you try to tackle this subfield.
Background Information for those relatively new to RL
Getting-started links from the
Reinforcement Learning Repository. Some links are broken but the surviving resources are good.
Deep understanding of game theory isn’t important for most work in machine learning of games, but some knowledge can only help. Game theory gave us fundamental terminology like “zero sum”, “perfect information” and “minimax”.
Game theory - Wikipedia
The encyclopedia article takes a refreshingly broad view of the range and applications of game theory, but of course it doesn’t go into detail.
Economic and Game Theory -
David Levine
The site includes getting-started information and a good
collection of links,
as well as specialist resources.
The emphasis on economics is moderate.