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Pattern Recognition and Analysis

Graduate · Historical course

Fall 2006

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.

Readings

  • Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern Classification . New York, NY: John Wiley & Sons, 2000. ISBN: 9780471056690.
    Listed reading
  • Microsoft® PowerPoint® slides, errata and other useful information about the textbook can be found here. The textbook is referred to as DHS further below.
    Listed reading
  • SES #: 1 · READINGS: DHS. Chapters 1 and A.1-A.2.
    Reading assignment
  • SES #: 2 · READINGS: DHS. Chapters A.2-A.5.
    Reading assignment
  • SES #: 3 · READINGS: DHS. Chapters 2.1-2.4. (can skip 2.3.1, 2.3.2)
    Reading assignment
  • DHS. Chapters 2.5-2.7.
    Listed reading
  • SES #: 5 · READINGS: DHS. Chapters 2.8.3, 2.9, and 3.1-3.2.
    Reading assignment
  • SES #: 6 · READINGS: DHS. Chapters 3.1-3.5.1.
    Reading assignment
  • Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach . Upper Saddle River, NJ: Prentice Hall, 2002, chapter 21. ISBN: 9780137903955.
    Listed reading
  • DHS. Chapters 3.7.1-.3 and 3.8.
    Listed reading
  • Turk, M., and A. Pentland. “Eigenfaces for Recognition.” Journal of Cognitive Neuroscience 3, no. 1 (1991): 71-86.
    Recommended
  • DHS. Chapters 2.11.
    Recommended
  • Belhumeur, P. N., J. P. Hespanha, and D. J. Kriegman. “Eigenfaces Vs. Fisherfaces: Recognition Using Class Specific Linear Projection.” IEEE Transactions on Pattern Analysis and Machine Intelligence 19, no. 7 (1997): 711-720.
    Recommended
  • 11 · Rabiner, L. R. “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.” Proceedings of the IEEE 77, no. 2 (1989): 257-286.
    Recommended
  • 12 · Rabiner, L. R. “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.” Proceedings of the IEEE 77, no. 2 (1989): 257-286.
    Recommended
  • 13 · DHS. Chapters 2.10 and 3.9.
    Recommended
  • DHS. Chapters 10.2-10.4.2.
    Recommended
  • Klarreich, Erica. “Election Selection.” Science News 162, no. 18 (2002): 280.
    Recommended
  • 16 · DHS. Chapters 4.1-4.6, DHS. Chapters 5.1-5.5.1, and 5.8.1.
    Recommended
  • 17 · DHS. Chapters 6.1-6.6 and 6.8.
    Recommended
  • 21 · DHS. Chapters 10.4.3, 10.4.4, and 10.6-10.10.
    Recommended
  • DHS. Chapters A.7 and 8.1-8.4.
    Recommended
Sources