Date	Topics	Readings
Aug 31: Lecture 1	Overview of Pattern Recognition, Model selection, Over-fitting, Classification/Regression examples, Bayesian probabilities, ML estimation, MAP.	Slides on course organization Lecture 1's slides Learn more about the No Hands Across America project Chapter 1: Sections 1.1, 1.2, 1.3. Chapter 2: Section 2.3 (to review the Gaussian distribution)
Sept 7: Lecture 2	Curse of dimensionality, Decision theory, Linear models for classification, discriminant functions. Homework 1	Lecture 2's slides Sections: 1.4, 1.5.
Sept 14: Lecture 3	Review of Probability Theory and the Gaussian Distribution. Fisher's linear discriminant; Perceptron.	Review Perceptron
Sept 21: Lecture 4	Linear model for classification: probabilistic generative models; probabilistic discriminative models. Homework 1 due	Lecture 4's slides
Sept 28: Lecture 5	Backpropagation Homework 2 Download the data	Backpropagation Project Guidelines Ideas for Projects
Oct 5: Lecture 6	More on Backpropagation Discussion of solutions of homeworks Homework 2 due
Oct 12: NO CLASS
Oct 19: Lecture 7	Midterm
Oct 26: Lecture 8	Principal Component Analysis; Kernel Machines;	PCA - part 1 PCA - part 2 PCA - part 3 Introduction to kernel methods
Nov 2: Lecture 9	Support Vector Machines	Handout
Nov 9: Lecture 10	Clustering Homework 3	Clustering
Nov 16: Lecture 11	One-class SVMs. Advanced Topics: an Overview.	One-class Support Vector Machines One-class SVM for Learning in Image Retrieval Advanced Topics
Nov 23: Lecture 12	Homework 3 due: in class problem solving
Nov 30: Lecture 13	Final
Dec 7: Lecture 14	Project presentations: Zeehasham Rasheed; Jason Southerland; Anveshi Charuvaka; Lewis Dalton; Jonathan Davis; Arezou Koohi; Mark Murphy; Joel Njanga; Robert Noteboom; Kenneth Wright.
Dec 14: 7:30-10:00pm	Project report due Project presentations: Steven Baehr; John Knowles; Tom Gatesman; Keni Patel; Chaitanya Yavvari; Vivek Rao; Jagadeesh Krishnaiah; Sandeep Talwar; Jeffrey Chiang; Carl Lee; Greg Gyor.