Examines how algorithms, probability theory, and statistical machine learning are used in computational molecular biology. Topics may include dynamic programming, graphs, Bayesian methods, hidden Markov models, classification, and their applications in sequence alignment, genome assembly, and protein localization. PREREQUISITES
