SYLLABUS
Contact Information:

Time	TTH: 8 - 9:15 AM
Place	106 Simrall
Instructor	Joseph Picone, Professor
Office	228 Simrall
Office Hours	available after class or any other times by appointment
Phone	662-312-4209
Email	joseph.picone@gmail.com
Instant Messaging	Google Talk: joseph.picone@gmail.com
Class Alias	ece_8423@ece.msstate.edu
URL	http://www.ece.msstate.edu/research/isip/publications/courses/ece_8423
Textbook (Reference)	P.M. Clarkson, Optimal and Adaptive Signal Processing, CRC Press, ISBN: 0-8493-8609-8 (Hardcover), 1993 (additional material available at Google Books)
Suggested References	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) X. Huang, A. Acero, and H.W. Hon, Spoken Language Processing - A Guide to Theory, Algorithm, and System Development, Prentice Hall, ISBN: 0-13-022616-5, 2001.
Prerequisite	ECE 3163 (Signals and Systems) or the consent of the instructor.
Other Reference Materials	ECE 8443 - Pattern Recognition: covers many of the same basic concepts from a statistical modeling point of view. ECE 8643 - Fundamentals of Speech Recognition: describes many of the same concepts from a speech recognition perspective.

Grading Policies:

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

Exams:

We will have two 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 does not formally count towards your grade.

Schedule:

Class	Date	Sections	Topic
1	Tuesday, August 19, 2008	1.1 - 2.6	Random Signals and Systems Review
2	Thursday, August 21, 2008	3.1	Optimal Estimation Procedures
3	Tuesday, August 26, 2008	3.2	Least-Squares Filter Design
4	Thursday, August 28, 2008	3.3	Applications of LMS Filters
5	Tuesday, September 02, 2008	4.1 - 4.2	The LMS Adaptive Filter
6	Thursday, September 04, 2008	5.1 - 5.2	Adaptive Noise Cancellation
7	Tuesday, September 09, 2008	5.3 - 5.4	Time-Delay Estimation
8	Thursday, September 11, 2008	6.1 - 6.2	LMS Variants
9	Tuesday, September 16, 2008	6.3	Recursive Least Squares Algorithms
10	Thursday, September 18, 2008	6.4 - 6.7	IIR and Lattice Filters
11	Tuesday, September 23, 2008	7.1 - 7.3	Periodograms and Blackman-Tukey Spectral Estimation
12	Thursday, September 25, 2008	7.4 - 7.5	Parametric Spectral Estimation
13	Tuesday, September 30, 2008	8.1 - 8.3	Array Signal Processing
14	Thursday, October 02, 2008	8.4	Adaptive Systems
N/A	Tuesday, October 07, 2008	N/A	Fall Break
15	Thursday, October 09, 2008	1.1 - 8.4	Mid-Term Exam
16	Tuesday, October 14, 2008	See Notes	EM and Simple Regression
17	Thursday, October 16, 2008	See Notes	Bias and Variance of the MLLR Estimate
18	Tuesday, October 21, 2008	See Notes	MLLR for HMMs
19	Thursday, October 23, 2008	See Notes	Practical Issues in MLLR
20	Tuesday, October 28, 2008	See Notes	MLLR and MAP - Another Look
21	Thursday, October 30, 2008	See Notes	Maximum A Posteriori Adaptation
22	Tuesday, November 04, 2008	See Notes	MAP for HMMs
23	Thursday, November 06, 2008	See Notes	Conjugate Priors and MAP Variance Estimation
24	Tuesday, November 11, 2008	See Notes	Discriminative Training
25	Thursday, November 13, 2008	See Notes	Discriminative Adaptation
26	Tuesday, November 18, 2008	See Notes	Unsupervised Estimation of Discriminative Transforms
27	Thursday, November 20, 2008	See Notes	Unsupervised Adaptation Using Large Amounts of Data
28	Tuesday, November 25, 2008	See Notes	State of the Art Systems
N/A	Thursday, November 27, 2008	N/A	Thanksgiving Break
29	Tuesday, December 02, 2008	See Notes	Sound Localization Using Beamforming
30	Saturday, December 06, 2008	Comprehensive	Final Exam

Homework:

Since this is an 8XXX class, homework will consist mainly of additional outside reading.