This introduction currently provides brief introductions to only a few of the key concepts in artificial intelligence. Other concepts that might be covered in the future include:
- Neural networks - possible topics include backpropagation, self-organising maps and Hopfield networks.
- Rule based systems - both backward chaining (e.g. Prolog) and forward chaining (e.g. RETE).
- Markov chains.
- Bayesian networks.
- Philosophy of AI.
- More in-depth coverage of the history of AI - including important researchers (e.g. Alan Turing) and interesting applications (e.g. ELIZA).
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Many educational institutions have made the content of their artificial intelligence courses available online for free. Some starting points for online study are:
Below are some books that give a general introduction to artificial intelligence:Artificial Intelligence: A Modern Approach
Written by Russel Norvig, this textbook is commonly referenced by introductory and intermediate artificial intelligence courses. Includes a wide range of artificial intelligence concepts. Coverage of each concept is supplemented by details of real-world usage, suggestions for further reading and exercises.
Written by Rob Callan, this book provides an introduction to artificial intelligence. Topics covered include: neural networks, genetic algorithms, decision tree learning and fuzzy logic. Although less extensive than Norvig's "A Modern Approach" this book still provides a broad introduction and is arguably an easier read.
Programming Collective Intelligence
Without the historical or philosophical perspectives provided by Norvig or Callan, and with the mathematical details restricted to the appendix, this book by Toby Segaran concentrates on practical examples of machine learning algorithms. The Python programming language is used to demonstrate how to apply clustering and filtering techniques to data obtained from real-world applications (e.g. Yahoo!, Facebook and eBay).