Course Description and Prerequisities

COMP 540 is about learning models from data. The course is designed to give you a foundational understanding of modern algorithms in learning and data mining, as well as hands-on experience with its applications in science and engineering.

The course assumes prior background in discrete mathematics, algorithms, linear algebra, probability and statistics as well a facility with programming. COMP 440 is highly recommended as a pre-requisite. COMP 540 can be taken as grounding for future research in machine learning or to gain familiarity with machine learning methods for application in other fields.

Texts and Reading Material

The course textbook is Pattern recognition and machine learning by Christopher Bishop. Other recommended texts on statistical machine learning include:

Machine learning:a probabilistic perspective by Kevin Murphy, 2012.
The elements of statistical learning T. Hastie, R. Tibshirani and J. Friedman, Springer, 2001.
Pattern Recognition 2nd edition, R. O. Duda and P. E. Hart and D. G. Stork, Wiley-Interscience, 2001.

Last modified: 2 January 2014 by

Devika Subramanian
devika@rice.edu