### Schedule, Lecture Notes, Assignments

 Date January 9 January 11 Lecture Notes Suggested Reading HW and Solutions 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 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 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 Lecture 4: Bayesian Decision Theory Continued Assignment1 , due Feb. 1 Lecture 5: Finish Bayesian Decision Theory and [2 S/PP] [4 S/PP] Gaussian Random Variables DHS Sections 2.5, 2.6 Lecture 6: Finish Gaussian Random Variables Lecture 7: Maximum-Likelihood Parameter Estimation [2 S/PP] [4 S/PP] DHS Sections 3.1, 3.2 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 Lecture 9: Continue Nonparametric Density Estimation 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 Lecture 11: Curse of Dimensionality, Dimensionality Reduction with PCA [2 S/PP] [4 S/PP] Ch. 3.7 and 3.8.1 Lecture 12: Fisher Linear Discriminant and MDA [2 S/PP] [4 S/PP] Ch. 3.8.2 and 3.8.3 Finish Lecture 12. Lecture 13: Linear Discriminant Functions [2 S/PP] [4 S/PP] Ch. 5.1-5.9 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 Lecture 15: Cross-validation and More on Cross-validation Data for the "competition" final project Lecture 16: Support Vector Machines [2 S/PP] [4 S/PP] Lecture 17: Finish Support Vector Machines [2 S/PP] [4 S/PP] Lecture 18: Neural Networks [2 S/PP] [4 S/PP] Lecture 19: Neural Networks continued [2 S/PP] [4 S/PP] Lecture 20: Radial Basis Function Networks and Ensemble Learning slides 2-11, slides 1-9 Lecture 21: Bagging and Boosting [2 S/PP] [4 S/PP] Lecture 22: Unsupervised Learning and Clustering [2 S/PP] [4 S/PP] Lecture 23: Low-dimensional Representations of high dimensional data [2 S/PP] [4 S/PP]