ECE1529 GENERAL INFORMATION

INSTRUCTOR

Prof. K. N. (Kostas) Plataniotis

OBJECTIVES

This course presents an introduction to the principles and application of adaptive and learning systems theories. Topics covered include:

• Decision theory
• Sufficiency and minimum variance unbiased estimators
• Neyman-Pearson theory
• Bayes decision theory
• Maximum likelihood estimation
• Bayes estimation theory
• Conditional expectation
• Linear estimation theory
• Kalman filters & smoothers
• Wiener filter
• Unsupervised learning

LECTURE HOURS

TBA
TBA
(First Lecture: Week of Monday, September 15, 2003)

REFERENCES

TEXTBOOK

Todd K. Moon & Wynn C. Stirling
Mathematical Methods and Algorithms for Signal Processing
Prentice Hall, Upper Saddle River, N.J., ISBN 0-201-36186-8, 2000.

ADDITIONAL REFERENCES

T.Kailath, A.H. Sayed, B. Hassibi
Linear Estimation
Prentice Hall, Upper Saddle River, N.J., 2000

Richard O. Duda, Peter E. Hart, David G. Stork
Pattern Classification (2nd Edition)
Wiley, N.Y., October 2000, ISBN 0-471-05669-3

S.M. Kay
Fundamentals of Statistical Signal Precessing: Estimation
Prentice Hall, 1998

K. Fukunaga
Introduction to Statistical Pattern Recognition (2nd Edition)
Academic Press, N.Y., 1990

H.L. Van Trees
Detection, Estimation and Modulation Theory, Part I
Wiley, 2001, ISBN, 0-471-09517-6

B.D.O. Anderson, J.B. Moore
Optimal Filtering
Prentice Hall, N.J., 1979

PREREQUISITES

Basics of:

• probability and random processes
• linear algebra
• signal processing
• digital communications

---

EVALUATION SCHEME

Final grades will be determined on the basis of assignments/take-home exam and final examination

Assignments	10%
Take-home Exam/Project	40%
Final Examination	50%

---