Prerequisites: Minimum grade of C- in CMSC351 and minimum grade of C- in any STAT400-level course; or DATA400; or ENEE324.

Covers statistical inference and machine learning methods for analyzing genomic data. Examples of topics covered will include maximum likelihood(including composite and pseudo-likelihood functions), expectation-maximization, clustering algorithms, hidden markov models, statistical testing, MCMC and variational inference. Our focus will be on how these techniques are utilized to solve biological problems and the practical challenges that arise when analyzing large genomic data sets.