It is
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) |