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6.867

Machine Learning

Graduate · Historical course

Fall 2006

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Readings

  • Bishop, Christopher. Neural Networks for Pattern Recognition . New York, NY: Oxford University Press, 1995. ISBN: 9780198538646.
    Listed reading
  • Duda, Richard, Peter Hart, and David Stork. Pattern Classification . 2nd ed. New York, NY: Wiley-Interscience, 2000. ISBN: 9780471056690.
    Listed reading
  • Hastie, T., R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction . New York, NY: Springer, 2001. ISBN: 9780387952840.
    Listed reading
  • MacKay, David. Information Theory, Inference, and Learning Algorithms . Cambridge, UK: Cambridge University Press, 2003. ISBN: 9780521642989. Available on-line here . ↗
    Listed reading
  • Mitchell, Tom. Machine Learning . New York, NY: McGraw-Hill, 1997. ISBN: 9780070428072.
    Listed reading
  • Cristianini, Nello, and John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods . Cambridge, UK: Cambridge University Press, 2000. ISBN: 9780521780193.
    Recommended
  • Burges, Christopher. “A Tutorial on Support Vector Machines for Pattern Recognition.” Data Mining and Knowledge Discovery 2, no. 2 (June 1998): 121-167.
    Recommended
  • Boyd, Stephen, and Lieven Vandenberghe. Convex Optimization . Cambridge, UK: Cambridge University Press, 2004. ISBN: 9780521833783.
    Recommended
  • Schapire, Robert. “A Brief Introduction to Boosting.” Proceedings of the 16 th International Joint Conference on Artificial Intelligence, 1999, pp. 1401-1406.
    Recommended
  • Bartlett, Peter, Yoav Freund, Wee sun Lee, and Robert E. Schapire. “Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods.” Annals of Statistics 26, no. 5 (1998): 1651-1686.
    Recommended
  • Shi, Jianbo, and Jitendra Malik. “Normalized Cuts and Image Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no. 8 (2000): 888-905.
    Recommended
  • Rabiner, Lawrence R. “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.” Proceedings of the IEEE 77, no. 2 (1989): 257-286.
    Recommended
  • Buy at MIT Press Heckerman, David. “A Tutorial on Learning with Bayesian Networks.” In Learning in Graphical Models by Michael I. Jordan. Cambridge, MA: MIT Press, 1998. ISBN: 9780262600323. ↗
    Recommended
  • Cover, Thomas M., and Joy A. Thomas. Elements of Information Theory . New York, NY: Wiley-Interscience, 1991. ISBN: 9780471062592.
    Listed reading
Sources