Learning

 

(*) We should add something from the Learning course CS221 (look at the old notes).

• [Dietterich1990b] Machine Learning.
  An excellent tutorial to machine learning. Covers almost the whole field.
• [Shavlik and Dietterich1990a] Readings in Machine Learning.
  Read the introduction pages to each of the chapters.
• [Shapiro and Eckroth1992] The Encyclopedia of Artificial Intelligence, Read the entry ``Learning, Machine,'' pages 785-805.
  An overview of machine learning. The different dimensions and paradigms.
• [Mitchell1981] Generalization as search.
  A well-written paper that views concept formation as a search problem.

• [Quinlan1990] Induction of Decision Trees
  A good survey paper on the problem of induction trees with discussions on noise, unavailable attributes, multiple attributes. The ID3 system is described in detail.
• [Fahlman and Hinton1987] Connectionist architectures for artificial intelligence.
  An introduction to the connectionist school of thought.
• [Koza1992] Genetic Programming : on the programming of computers by means of natural selection Chapters 3,5.
  These chapters provide a basic introduction to genetic algorithms and genetic programming. The formula for on page 43 is wrong. Should be (see [Goldberg1989]).