This seminar course will include background lectures by the instructors, invited guest lectures, and presentations of readings by students in the course. Students will be expected to present at least one major paper during the term. This course covers the machine learning foundations of computational biology and studies important research problems in the areas of computational and systems biology. The goal is for the students to acquire breadth as well as some depth. Tentative topics include: * Fundamental algorithmic techniques including dynamic programming, Bayesian models, Gibbs sampling, expectation maximization, hidden Markov models, clustering, dimensionality reduction, Bayesian networks, Markov networks, and approximate inference on graphical models. * Biological problems including motif discovery, gene expression analysis, biological network reconstruction, network analysis, comparative genomics, metagenomics analysis, and phylogenetics.

Usually Offered:	Spring
Credit:	3 hours (class)
Prerequisite:	Basic probability and statistics and introductory biology or machine learning coursework / research experience or permission of instructor
University Catalog:	CS 590B
Schedule:	Spring 2008 Instructor: Alan Qi