Date | Lecture Notes | Suggested Reading | HW and Solutions | |
---|---|---|---|---|

January 9 | Lecture 1: [2 S/PP] [4 S/PP]. Course Overview, Review of Probability and Statistics | Chapter 1 up to section 1.5 and review your favorite statistics/probability book | ||

January 11 | Lecture 2: [2 S/PP] [4 S/PP]. Review of Linear Algebra and Intro to Matlab | Matlab Primer Link and in PDF format Matlab Primer | ||

January 16 | Lecture 3: [2 S/PP] [4 S/PP]. Bayesian Decision Theory | DHS Sections 2.1,22.,2.3 (but not sections with stars), 2.4 | ||

January 18 | Lecture 4: Bayesian Decision Theory Continued | Assignment1 , due Feb. 1 | ||

January 23 | Lecture 5: Finish Bayesian Decision Theory and [2 S/PP] [4 S/PP] Gaussian Random Variables | DHS Sections 2.5, 2.6 | ||

January 25 | Lecture 6: Finish Gaussian Random Variables | |||

January 30 | Lecture 7: Maximum-Likelihood Parameter Estimation [2 S/PP] [4 S/PP] | DHS Sections 3.1, 3.2 | ||

February 1 | Lecture 8: Nonparametric Density Estimation [2 S/PP] [4 S/PP] | DHS Sections 4.1, 4.2, 4.3.3,4.3.4,4.3.6,4.4,4.5(except 4.5.1, 4.5.2), 4.6.1 | ||

February 6 | Lecture 9: Continue Nonparametric Density Estimation | |||

February 8 | Lecture 10: Finish Nonparametric Density Estimation | Assignment2 , due Feb. 22. Files for the assignment: A2.mat and display_image.m Also some face images for the last problem | ||

February 13 | Lecture 11: Curse of Dimensionality, Dimensionality Reduction with PCA [2 S/PP] [4 S/PP] | Ch. 3.7 and 3.8.1 | ||

February 15 | Lecture 12: Fisher Linear Discriminant and MDA [2 S/PP] [4 S/PP] | Ch. 3.8.2 and 3.8.3 | ||

February 18 | Finish Lecture 12. Lecture 13: Linear Discriminant Functions [2 S/PP] [4 S/PP] | Ch. 5.1-5.9 | ||

March 6 | Finish Lecture 13. Lecture 14: Continue Linear Discriminant Functions [2 S/PP] [4 S/PP] | Assignment 3 , due March 20. Files for the assignment: P3.mat and visualize_pc.m | ||

March 8 | Lecture 15: Cross-validation and More on Cross-validation | Data for the "competition" final project | ||

March 13 | Lecture 16: Support Vector Machines [2 S/PP] [4 S/PP] | |||

March 15 | Lecture 17: Finish Support Vector Machines [2 S/PP] [4 S/PP] | |||

March 20 | Lecture 18: Neural Networks [2 S/PP] [4 S/PP] | |||

March 22 | Lecture 19: Neural Networks continued [2 S/PP] [4 S/PP] | |||

March 27 | Lecture 20: Radial Basis Function Networks and Ensemble Learning slides 2-11, slides 1-9 | |||

March 29 | Lecture 21: Bagging and Boosting [2 S/PP] [4 S/PP] | |||

April 3 | Lecture 22: Unsupervised Learning and Clustering [2 S/PP] [4 S/PP] | |||

April 10 | Lecture 23: Low-dimensional Representations of high dimensional data [2 S/PP] [4 S/PP] | |||