An introductory course in machine learning for development of intelligent knowledge based systems. The first half of the course will focus on basic taxonomies and theories of learning, algorithms for concept learning, statistical learning, knowledge representation, pattern recognition, and reasoning under uncertainty. The second half of the course will survey some basic topics in combining multiple models, learning from time series, learning to reason, and selected applications in knowledge discovery and data mining, especially in bioinformatics.
The course will include several written and programming assignments and a term project option for graduate students. Ancillary readings will be assigned; students will write a brief synopsis and review for one of these papers every other lecture.
This will be my sixth offering of machine learning (the first five being in 1999, 2001, 2002, 2003, and 2005). This time I'm cross-listing it with my Advanced AI course (CIS 830), and am giving approximately equal time to graphical models, genetic and evolutionary computation (especially genetic programming, but with some genetic algorithms coverage), and artificial neural networks.