The seminar aims to relate this research to other Machine Learning applications that have been researched in the Department, and to explore issues in their common methodology. This includes comparing the many different statistical fitting methods (Bayesian, max-likelihood, simple frequentist, and more) that can be used and judged within this same model. No background in chess is assumed---the basics of chess programs will be covered in the initial series of lectures by me.
Here are a two-page description and a longer overview of the research, the latter with some mathematical details. My homepage links my public anti-cheating site, papers, talks, New York Times article, and other pages; students in the seminar will be given access to my private sites where testing is done. The last section of the overview includes some possible seminar topics and projects within this research, but students will be equally welcome to give presentations relating it to machine-learning related topics they have had in other courses.
Students are expected to participate in discussions and give at least two hours of presentations. Grading is S/U, 1--3 credits.