SYLLABUS
Contact Information:

Lecture Time	MWF: 11 - 11:50 AM
Lecture Place	129 Simrall
Lecturer	Joseph Picone, Professor
Lecturer Office	228 Simrall
Lecturer Office Hours	available after class or any other time by appointment
Lecturer Phone	662-312-4209
Lecturer Email	joseph.picone@gmail.com
Lecturer Instant Messaging	Google Talk: joseph.picone@gmail.com
Recitation Lecture Time	TBD
Recitation Lecture Place	TBD
Recitation Lecturer	TBD
Recitation Lecturer Office	TBD
Recitation Lecturer Office Hours	TBD
Recitation Lecturer Phone	TBD
Recitation Lecturer Email	TBD
Recitation Lecturer Instant Messaging	TBD
Class Alias	ece_3163@ece.msstate.edu
URL	http://www.ece.msstate.edu/research/isip/publications/courses/ece_3163
Textbook (Reference)	R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, Second Edition, Wiley Interscience, ISBN: 0-471-05669-3, 2000 (supporting material available at http://rii.ricoh.com/~stork/DHS.html)
Suggested Reference	D. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003 (also available at http://www.inference.phy.cam.ac.uk/mackay/itprnn/book.html)
Prerequisite	Statistics, Signal Processing, many years of research, and attendance at a lecture where the speaker proudly exclaimed "PCA Fails!"
Other Reference Materials	Pattern Recognition in Java: an applet designed to demonstrate fundamental concepts of pattern recognition. Internet Links: Godfried T. Toussaint's online resources that include lots of valuable links and course notes. Human Computer Interface Design: a multi-university course sponsored by the National Science Foundation.

Grading Policies:

Item:	Date:	Weight:
Mid-Term Exam	TBD	50%
Final Exam	TBD	50%
Special Projects	Unspecified	10% per project

Exams:

We will have two in-class exams in this course (a mid-term and a final). Each will be closed books and notes. You will be allowed one page (double-sided) of notes. Calculators or other computing devices will not be allowed (or needed). The exams will require critical thinking. They will consist of one to two problems for which you will supply extended written solutions. The quality and clarity of your solution will be as important as whether you obtained the correct answer.

In addition, students can select and execute special projects on topics that are of interest to the class or two their research. These will be negotiated on an individual basis with the instructor, and must be agreed to in writing before the work can proceed. Typically in this class interesting issues arise that merit extended analysis. Students can earn extra credit by exploring these topics. Projects commonly involve a theoretical derivation, a computer simulation and a four-page paper summarizing the findings.

Attendance Policy:

Attendance is encouraged of course, but not formally count towards your grade. Most students tend not to do well in this class without frequent interaction with the instructor and the other students on the material.

Schedule:

Class	Date	Sections	Topic
1	01/10	Chapter 1	Course Introduction
2	01/15	2.1 - 2.3	Bayes Decision Theory
3	01/17	2.4 - 2.5	Multivariate Gaussian Densities
4	01/22	2.6 - 2.8	Discriminant Functions For Gaussians
5	01/24	2.9, 3.1 - 3.2.3	Discrete Features, Maximum Likelihood
6	01/29	3.2.3 - 3.4	Bayesian Estimation
7	01/31	3.5, 3.6	Generalized Bayesian Parameter Estimation, Sufficient Statistics
8	02/05	3.7, 3.8.1	Problems of Dimensionality, Principal Components Analysis
9	02/07	3.8.2, 3.8.3	Linear Discriminant Analysis
10	02/12	10.13	Heteroscedastic Linear Discriminant Analysis Independent Component Analysis
11	02/14	3.9	The Expectation Maximization Algorithm
12	02/19	3.10.1 - 3.10.5	Hidden Markov Models
13	02/21	3.10.6	The Baum-Welch Algorithm
14	02/26	4.1 - 4.9	Nonparametric Techniques
15	02/28	1.1 - 3.9	Mid-Term
16	03/04	5.11	Support Vector Machines
17	03/06	6.1 - 6.4	Neural Netwoks
18	03/18	8.1 - 8.8	Decision Trees
19	03/20	9.1 - 9.4	Resampling for Estimating Statistics
20	03/25	9.5 - 9.7	Resampling for Classifier Design
21	03/27	N/A	Reinforcement Learning
22	04/01	10.1 - 10.4	Clustering
23	04/03	10.5 - 10.9	Hierarchical Clustering
24	04/08	10.10 - 10.14	Networks, Maps and Clustering
25	04/10	N/A	Discriminative Training
26	04/15	N/A	Particle Filters
27	04/17	N/A	PAC-Bayes Bounds
28	04/22	N/A	Statistical Significance and Confidence
29	04/24	N/A	Examples of Pattern Recognition in Speech Recognition
30	05/01	Comprehensive	3 PM to 6 PM

Homework:

Homework will be assigned but not graded. Solutions are available online.

Number	Chapter: Problem(s)
1	Chapter 2: 2, 3, 8, 10
2	Chapter 2: 15, 21, 23, 24
3	Chapter 2: 37, 40, 43, 44
4	Chapter 3: 1, 4, 11, 13, 16
5	Chapter 3: 17, 24, 38, 41, 50
6	Chapter 4: 2, 3, 10, 13, 14
7	Chapter 5: 9, 32, 33
8	Chapter 6: 2, 10, 21
9	Chapter 8: 5, 8, 19
10	Chapter 9: 18, 20, 27
11	Chapter 10: 4, 5, 14