The course will be based on Lecture Notes, described in the Course Schedule.

Further recommended readings will be selected parts of the following books. All are available at www.amazon.com, or at the Fondren Library at Rice. You can also borrow my copies for short periods of times.

On neural networks
• Frederick Ham and Ivica Kostanic: Principles of Neurocomputing for Science & Engineering. McGraw-Hill, 2001.
• Simon Haykin: Neural Networks. A Comprehensive Foundation. McMillan, New Jersey, 1999. (2nd Edition)
• Christopher M. Bishop: Neural Networks for Pattern Recognition. Oxford University Press, 1995.

These books are also on reserve for this course in the Ares system of Fondren. Search for COMP 502, ELEC 502, or STAT 502.

On machine learning (neural and other)
• Christopher M. Bishop: Pattern Recognition and Machine Learning. Springer, 2007.

Supplemental background material
Probability:
• Sheldon M. Ross: First Course in Probability (recommended by an ECE graduate student)

Matrix algebra:
• Gene H. Golub and Charles F. Van Loan: Matrix Computations The John Hopkins University Press, 2nd edition, 1989.

Information theory:
• Thomas M. Cover and Joy. A, Thomas: Elements of Information Theory. Wiley Series in Telecommunications. Wiley and Sons, 1991.

Watch this page for update of recommended reading!