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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]

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		Introduction	Syllabus
Thursday	Pattern Recognition Systems Terminology Bayesian decision theory	2.1-2.4 Append A.4 2.8.3 2.3.1 and 2.3.2 will NOT be on the exam	Bayesian Decision Theory		Homework #1 (Due Sept. 21, 2017)

Week 2

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Bayesian decision theory (Continued)
Thursday	Numerical Examples Bayesian decision theory for normal density Classification of real datasets Naive Bayesian classifier Advanced topics	2.5, 2.6 2.9 (Self-reading) 2.7, 2.8.1, and 2.8.2 will NOT be on the exam	Numerical Examples 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 (Continued) Naive Bayesian classifier Advanced topics (Continued)		(Same as last time)
Thursday	Naive Bayesian Classifier Advanced topics (Continued) Maximum-likelihood estimation	3.1-3.2	Parameter Estimation		Homework #2 (Due 10/4/2017)

Week 4

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Maximum-likelihood estimation (Continued)		Parameter Estimation
Thursday	Bayesian estimation Nonparametric Techniques - Parzen Windows	3.3, 4.1-4.3	Parzen Windows

Week 5

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Nonparametric Techniques Parzen Windows (Continued)	4.1-4.3	Parzen Windows
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	Nonparametric Techniques (Continued) K-nearest neighbor rule Tangent distance (not required for exams) Reduced Coulomb Enegery Networks (not required for exams)	4.6, 4.8	Nearest-Neighbor Estimation		P: Programming Assignment #1 (Due 11/2/2017) 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)
Thursday	Linear Discriminant Function (continued)	Linear Discriminant Function			H: Homework #3 (Due 10/19/2017)

Week 7

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Generalized linear discriminant functions Kernel trick Support vector machines	5.12.1-5.12.2	Kernel Trick and SVM
Thursday	Support vector machines (Continued) 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	Generalized linear discriminant functions Kernel trick Support vector machines Boosting & Component Analysis	5.12.1-5.12.2 9.5.2 3.8.1-3.8.3	Boosting & Component Analysis	Small SVM Example in Matlab
Thursday	Multilayer neural networks	6.1-6.5	Neural Networks

Week 9

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Multilayer neural networks (Continued) Neural Network Examples		6.8, 6.9 (Not on exam)	Neural Networks Neural Network Examples	H:: Homework # 4 (11/14/2017)
Thursday	Deep Neural Networks		Deep Neural Networks		Term Project (Brief proposal due: Nov. 9, 2017 Full report due: 5:00pm, Thursday, December 15, 2017)

Week 10

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Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Deep Neural Networks (continued) Decision Trees	8.2-8.4	Decision Trees
Thursday	Decision Trees (Continued)	P: Programming Assignment #2 (Due 12/5/2017)

Week 11

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Algorithm-Independent Machine Learning	Chapter 9	Algorithm-Independent Learning
Thursday	Midterm exam review	Chapters 1-6, 8.2-8.4, and 9.2-9.7 (Some sections are excluded, see the slides for detail)	Midterm Review

Week 12

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday (11/14/2017)	Midterm exam review (Continued) Questions and answers Midterm Paper Distribution (Take-home exam)			Midterm Fall 2017
Thursday (11/16/17)	No class; time to work on the midterm exam

Week 13

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Midterm exam due at the beginning class Unsupervised learning (Continued)	Chapter 10	Unsupervised Learning
Thursday	Thanksgiving Holiday; no class

Week 14

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Unsupervised learning (Continued)	Chapter 10
Thursday	Introduction to Reinforcement Learning	Reinforcement Learning

Week 15

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Tuesday	Syntactic Pattern Recognition	Chapter 10	Syntactic Pattern Recognition
Thursday	Case Studies and Summary		Extreme Event Modeling Summary

Final Exam Week

Date	Topics	Reading	Lecture Notes	Handout	Assignments
Friday	Final Project				Due: 5:00pm, Dec. 15, 2017

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Last modified on August. 25, 2017