Reading Assignments


It is important that you read the assigned chapters (if you have the books) even though there is nothing to be turned in for grading.

DHS = Duda, Hart, and Stork
HTF = HAstie, Tibshirani, and Friedman
SC = Shawe-Taylor and Chistiaini
Introduction
Parameter Estmation Technqiues
Non-parametric Estimation Techniques
Linear Discriminant Function
Regression
Knernel Methods
Maximum Margin Classifiers
Dimension Reduction Techiques
Decision Trees
Perceptrons
Performance
Mixture models
Unsupervised clustering
DHS, Appendix A, Mathmatical Foundation, Chapter 1, HTF Chapter 1
DHS, 3.1 - 3.5 (MLE), 2.1 - 2.5 (Bayesian)
DHS, 4.1 - 4.6 (density estimation)
DHS, 5.1 - 5.6, HFT Chapter 4 (linear discriminant functions)
HFT Chapter 3 (Regression)
SC, Chapters 2, 3, 9, 10, 11, HTF Chapter 12
SC, Chapter 7, HTF Chapter 12
DHS, 3.8 (dimension reduction)
DHS, 8.1 - 8.3, HTF Chapter 9 (Decision tree methods)
DHS, 6.6 - 6.6, HTF Chapter 11 (perceptron learning)
DHS, 9.1 - 9.6, HTF Chapter 7 (performance & generalization)
DHS, 10.1 - 10.4 (mixture models)
DHS, 10.6 - 10.11, HTF Chapter 14 (unsupervised clustering & learning)

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