CSC2515 Fall 2002 - Weekly and Other Readings

Textbook

Weekly Readings

• September 11
  L.G. Valiant, A Theory of the Learnable [pdf, 9pages]

• Semptember 18 (choose one)
  R.A. Fisher, The Use of Multiple Measurements in Taxonomic Problems [pdf, 10pages] or
  J.R. Quinlan, Induction of Decision Trees [in class,14pages]

• September 25
  Michael Jordan, Why the logistic function? [pdf , ps.gz, 13pages]
  

• October 2
  Robert Tibshirani Regression shrinkage and selection via the lasso [pdf , ps.gz, 28pages]
  

• October 9
  Rumelhart, Hinton and Williams, Learning representation by backpropagating errors, (Nature, 1986) [in class, 4 pages].

• October 16
  Michael I. Jordan and Robert A. Jacobs (1994), Hierarchical Mixtures of Experts and the EM Algorithm [pdf , ps.gz, 36pages]
  Note: only read pages 1-10 this week.
• October 23
  rest of the Mixtures Experts article

• October 30
  Geoff Hinton and Radford Neal, A View of the EM Algorithm, Learning in Graphical Models (1998), [pdf , ps.gz, 14pages]
  

• November 6 and November 13
  Sam Roweis and Zoubin Ghahramani, A Unifying Review of Linear Gaussian Models, Neural Compuation (1999), [pdf 41pages]
  Skip sections 5.4, 6.2, 8, 9, A.3

• November 13
  Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988. [pdf7pages]

• November 20
  Golub, Heath, Wahaba, Generalized Cross Validation, Technometrics 1979. [in class, 9 pages]

• November 27
  Rob Shapire, The Strength of Weak Learnability., Machine Learning 1990. [in class, 31 pages]

• December 4
  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].
• A tutorial by Andrew Moore at CMU on Bayesian methods.
• Another tutorial by Andrew Moore on probabilities for machine learning and data mining.
• A short MATLAB tutorial.
• Pedro Domingos, Michael Pazzani, On the Optimality of the Simple Bayesian Classifier [pdf, 28pages]
• David Mackay, Maximum Likelihood and Covariant Algorithms for ICA [ps.gz, 15 pages]
• Zoubin Ghahramani and Geoff Hinton, The EM algorithm for Mixtures of Factor Analyzers [ps.gz, 8 pages]
• Leo Brieman, Bagging Predictors [pdf, 20 pages]
• David Wolpert, Stacked Generalization [ps.gz, 57 pages]
• Rob Shapire, Boosting Overview [ps.gz, 23 pages]

Draft Book Chapters