Spring 2010 - Tu, Th 2-3:15
Course Objectives: The course will introduce practical machine learning tools and techniques with applications to data mining using commercial, scientific, and web data sets. Techniques to be covered include: decision trees; learning rules; neural networks; Bayesian classification; support vector machines; association rules; and clustering. Students will acquire practical knowledge of these techniques through the use of the Weka software environment.
Textbook: Data Mining, Practical Machine Learning Tools and Techniques, Second Edition, I. H. Witten and E. Frank, Morgan-Kaufmann, 2005.
http://www.cs.waikato.ac.nz/ml/weka/book.html
Prerequisite Topics: good programming background, some familiarity with multivariable calculus, basic data structures and algorithms. Familiarity with basic probability concepts .
Lecture Handouts: