Optional Textbooks:

• Pattern Classification (2nd Edition)
  by Richard O. Duda, Peter E. Hart, David G. Stork
  Publisher: Wiley-Interscience; 2nd edition (October 2000)
  ISBN: 0471056693



• Machine Learning
  by Tom M. Mitchell
  Publisher: McGraw-Hill Science/Engineering/Math; (March 1, 1997)
  ISBN: 0070428077



• The Elements of Statistical Learning
  by T. Hastie, R. Tibshirani, J. H. Friedman
  Publisher: Springer Verlag; (August 9, 2001)
  ISBN: 0387952845



• Introduction to Machine Learning
  by Ethem Alpaydin
  To be published by The MIT Press
  (Two copies available in CS Library)

These books are available on reserve in the CS library.

Course topics:

• Introduction to Learning: Discriminitive Learning/Supervised Learning
  • - linear models
  • - decision trees
  • - nearest neighbor
  • - SVMs
• Generative Learning
  • - graphical models
  • - expectation maximization
  • - clustering
• Learning Theory
  • - VC dimension
  • - Bias/Variance tradeoff
• Symbolic/Logical Approaches
  • - version spaces
  • - genetic algorithms
  • - inductive logic programming
• More Complex Models
  • - hidden markov models (HMMs)
  • - markov random fields (MRFs)
  • - conditional random fields (CRFs)
  • - probabilistic relational models (PRMs)

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