ComS 672 Computational Models of Learning
COMPUTATIONAL MODELS OF LEARNING
Computational Models of Learning -- ComS 672 -- is a 3-credit, graduate
course offered by Professor Vasant Honavar in the Department of Computer Science at Iowa State University in
alternate spring semesters.
3 Credits. Offered Alternate Spring.
Prerequisites: ComS 572 or 472 or 474 or comparable background.
Design, Analysis, and Application of Programs that Learn from Experience.
Statistical, Syntactic, Information-Theoretic, Neural, Cognitive,
and Evolutionary models. Automated learning of classification rules,
programs, functions, relations, grammars, value functions, models,
skills, and behaviors. Computational learning theory (PAC, Maximum Likelihood,
Minimum Description Length and related frameworks). Learning from instances,
induction, deduction, reinforcement, and exploration. Incremental, multi-task,
multi-strategy learning. Selected applications in Scientific Data Analysis,
Data-Driven Knowledge Discovery and Theory Refinement, Bioinformatics,
Analysis and Control of Complex Dynamical Systems, Intelligent Agents and
Lectures: MW 9:00-10:30am, 217 Atanasoff Hall.
Office Hours: MW 2:00pm-3:00pm, 210 Atanasoff Hall.
Dr. Vasant Honavar
Artificial Intelligence Research Laboratory
Department of Computer Science and
210 Atanasoff Hall
Iowa State University
Voice: 515 294-1098, Fax: 515 294-0258
Weekly lecture outlines, notes,
and reading assignments
Recommended Textbooks and References
- Mitchell, T. 1997. Machine Learning New York: McGraw-Hill. (primary text)
- Langley, P. 1995. Elements of Machine Learning, Palo Alto, CA: Morgan Kaufmann.
- Bishop, C.M. 1996. Neural Networks for Pattern Recognition New York: Oxford University Press.
Bower, G.H. and Hilgard, E.R. 1981. Theories of Learning. New York: Prentice-Hall.
- Ripley, B.D. 1996. Pattern Recognition and Neural Networks New York: Cambridge University Press.
- Mitchell, M. 1996. An Introduction to Genetic Algorithms Cambridge, MA: MIT Press.
- Miclet, L. 1986. Structural Methods in Pattern Recognition Berlin: Springer Verlag.
- Natarajan, B. 1992. Machine Learning: A Theoretical Approach. Palo Alto, CA: Morgan Kaufmann.
- Kearns, M. and Vazirani, U. An Introduction to Computational Learning Theory. Cambridge, MA: MIT Press. (1994).
- Russell, S. and Norvig, P. 1995. Artificial Intelligence: A Modern Approach Englewood Cliffs, NJ: Prentice Hall.
- Sutton, R. and Barto, A. 1997. Reinforcement Learning Cambridge, MA: MIT Press.
- Recommended Reference Books (Several books on this list are available on reserve at the Iowa State University Library)
- Machine Learning Resources
Computer Science Resources
Other Useful Links
This page is maintained by: Dr. Vasant Honavar . Please send suggestions, additions, or changes to: firstname.lastname@example.org.