CSC2515 Fall 2004 - Weekly and Other Readings


There is no required textbook for the class.
Two recommended books that cover similar material are Hastie, Tibshirani, Friedman Elements of Statistical Learning and MacKay, Info Theory, Inference, and Learning Algorithms which is freely available online.

I will be handing out class notes as we go along.

Some classic papers will be assigned as weekly readings.

We will also be covering material similar to a variety of chapters from a few other books which I will point out in class.

Weekly Readings

  • September 14
    L.G. Valiant, A Theory of the Learnable [pdf, 9pages]
  • September 21
    R.A. Fisher, The Use of Multiple Measurements in Taxonomic Problems [pdf, 10pages]
  • September 28
    Michael Jordan, Why the logistic function? [pdf , ps.gz, 13pages]
  • October 5
    Robert Tibshirani Regression shrinkage and selection via the lasso [pdf , ps.gz, 28pages]
  • October 12
    Rumelhart, Hinton and Williams, Learning representation by backpropagating errors, (Nature, 1986). [pdf, 4pages]
  • October 19
    Michael I. Jordan and Robert A. Jacobs (1994), Hierarchical Mixtures of Experts and the EM Algorithm [pdf , ps.gz, 36pages]
  • October 26
    C.K.Chow and C.N. Liu, Approximating discrete probability distributions with dependence trees [pdf, 6 pages]
  • November 2
    Geoff Hinton and Radford Neal, A View of the EM Algorithm, Learning in Graphical Models (1998), [pdf , ps.gz, 14pages]
  • November 9
    Zoubin Ghahramani and Geoff Hinton, The EM algorithm for Mixtures of Factor Analyzers [ps.gz, 8 pages]
  • November 16
    Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988. [pdf7pages]
  • November 23
    Bradley Efron and Gail Gong, A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation The American Statistician, Vol. 37, No. 1. (Feb., 1983), pp. 36-48. [pdf13pages]
    (Note that there is a tiny typo in this paper: 2 lines below expression (3) on the 1st page, the bar is ommited from the x(i) on the right side of the equation.)
  • November 30
    Rob Shapire, The Strength of Weak Learnability., Machine Learning 1990. [pdf31pages]
  • December 7
    Corinna Cortes and Vladimir Vapnik, Support Vector Networks, Machine Learning 20(3): 273-297 (1995) [ps.gz31pages]

Additional Material

  • Probability and Statistics Review [ps.gz].
  • Some useful matrix identities and gaussian identities.
  • Andrew Moore at CMU has a tutorials page with many excellent mini-tutorials on various statistical machine learning topics.
    In particular, you might want to check out his tutorials on probability and density, and on Gaussian and Bayesian classifiers.
  • A short MATLAB tutorial.
  • This chapter from Numerical Recipes in C talks about linear programming. You might also be interested in the other material, especially from Chapter 10 and 15.

Draft Book Chapters

  • Linear Algebra, v1.3 [ps.gz].

    Extra Papers of Interest

    • Boser, Guyon, Vapnik, A Training Algorithm for Optimal Margin Classifiers, COLT 1992. [ps.gz9pages]
    • Sam Roweis and Zoubin Ghahramani, A Unifying Review of Linear Gaussian Models, Neural Compuation (1999), [pdf 41pages]
    • David Mackay, Maximum Likelihood and Covariant Algorithms for ICA [ps.gz, 15 pages]
    • Marina Meila, An accelerated Chow and Liu algorithm [ps.gz, 12 pages]
    • An article from Scientific American on Stein's Paradox. Another paper on this topic.
    • Golub, Heath, Wahaba, Generalized Cross Validation, Technometrics 1979. [pdf]
    • The voted perceptron algorithm is introduced in this paper by Freund and Schapire.

    [ Home | Course Information | Lecture Schedule/Notes | Textbook/Readings | Assignments/Tests | Computing | ]

    CSC2515 - Machine Learning ||