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Weekly Calendar

[Week 1] [Week 2] [Week 3] [Week 4] [Week 5]
[Week 6] [Week 7] [Week 8] [Week 9] [Week 10]
[Week 11] [Week 12] [Week 13] [Week 14] [Week 15] [Week 16]

Assignment code

E - Exercise (do not need to turn in).
H - Homework (need to turn in hardcopy).
P - Programming projects.
D - Specified assignments are due in class today.
M - Midterm.
F - Final.
S - Self study.

Week 1

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Motivations Organizational Issues General Introduction to Pattern Recognition Motivations	Chapter 1	Introduction	Syllabus
Thursday	Pattern Recognition Systems Terminology Bayesian decision theory	2.1-2.4 (except 2.3.2) Append A.4 2.8.3 2.3.1 will NOT be on exam	Bayesian Decision Theory

Week 2

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Bayesian decision theory (Continued) Numerical Examples		Numerical Examples		Homework #1 ( Word format) (Due ~~9/15/2009~~ 9/17/2009)
Thursday	Bayesian decision theory for normal density Classification of real datasets Naive Bayesian classifier Advanced topics	2.5, 2.6 2.9 (Self-reading) 2.10-2.12 (Will NOT be on exams)	Bayesian Classification for Normal Density	Example Matlab programs Wine Ocr Naive Bayesian classifier analysis paper (Not required) Spam filtering (From http://www.paulgraham.com/spam.html) (Proceedings of the first conference on North American chapter of the Association for Computational Linguistics, pp. 63-69, 2002.)

Week 3

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Bayesian decision theory for normal density Classification of real datasets Naive Bayesian classifier Advanced topics (Continued)		(Same as last time)
Thursday	Maximum-likelihood estimation	3.1-3.2	Maximum-likelihood

Week 4

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Maximum-likelihood estimation (Continued) Bayesian estimation	3.2-3.5	Parameter Estimation (Updated)		Homework #2 ( Word format) (Due ~~9/29/2009~~ 10/1/09)
Thursday	Parametric Methods (Continued) Nonparametric Techniques - Parzen Windows	4.1-4.3	Week 4 Thursday

Week 5

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Nonparametric Techniques	4.1-4.3	Parzen Windows (Updated)
Thursday	Nonparametric Techniques (Continued)	4.4, 4.5, 4.6.1 4.6.2,4.8 (Not required for exams)	Nearest-Neighbor Estimation

Week 6

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	K-nearest neighbor rule (continued) Tangent distance (not required for exams) Reduced Coulomb Enegery Networks (not required for exams)	4.6, 4.8	Same as last time		Homework #3 ( Word format) (~~Due 10/13/2009~~ 10/15/2009)
Thursday	Linear discriminant functions	5.1-5.2 5.4-5.7 5.12.1-5.12.2	Linear Discriminant Functions		Lab #1 ( Word format) (Due October 22, 2008) Iris dataset Iris training set Iris test set (Description) UCI wine dataset UCI wine training set UCI wine test set (Description) USPS ZIP dataset USPS ZIP training set USPS ZIP test set (USPS ZIP test set - small) (Description)

Week 7

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Linear discriminant functions (continued) Generalized linear discriminant functions Kernel trick Support vector machines	5.12.1-5.12.2	Kernel Trick and SVM
Thursday	Support vector machines Boosting Component analysis	5.12.1-5.12.2 9.5.2 3.8	Boosting & Component Analysis	Small SVM Example in Matlab

Week 8

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Component analysis (Continued) Multilayer neural networks	3.8.1-3.8.3 6.1-6.5	Neural Networks
Thursday	Multilayer neural networks	6.8, 6.9 (Not on exam)	Same as last time

Week 9

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Neural Networks (continued)	6.3-6.5,6.8	Same as last time		Homework #4 ( Word format) (Due 11/03/2009)
Thursday	Neural Networks (continued)	6.10 (Not on the exam)	Neural Network Application Examples

Week 10

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Decision Trees	8.2-8.4	Decision Trees		Lab 2 ( Word format) (Due 11/24/09) Term Project ( Word format) (Proposal due: 11/19/2009 Final report due: 5:00pm, 12/11/09)
Thursday`	Decision Trees (Continued) Algorithm-independent machine learning	Chapter 9 9.7 (Not required for the exam)

Week 11

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Algorithm-independent machine learning (Continued)	Chapter 9	Same as last time
Thursday	Midterm exam review	Chapters 1-6, 8.2-8.4, and 9.1-9.5 (Some sections are excluded, see the slides for detail)	Midterm Review

Week 12

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Questions and answers Unsupervised Learning	Chapter 10 (Not on the exam)	Unsupervised Learning
Thursday (Nov. 12)	Midterm		Hints	Midterm Fall 2009

Week 13

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Midterm exam extension; no lecture
Thursday	Unsupervised learning (Continued) Recognition with strings Syntactic Pattern Recognition	Chapter 10 8.5, 8.6-8.7	Same as last time Syntactic Pattern Recognition

Week 14

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Midterm exam discussion Syntactic Pattern Recognition (Continued)		Same as last time
Thursday	No Class		Happy Thanksgiving

Week 15

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Generative Models Synthesis-by-analysis Deformable template matching
Thursday	Case Studies and Summary

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Last modified on Aug. 24, 2009