Courses - Spring 2025
CMSC498Y
Selected Topics in Computer Science; Statistical Inference and Machine Learning Methods for Genomics Data
Credits:
3
Grad Meth:
Reg
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.
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.
Flagship Institution of the University System of Maryland
University of Maryland, College Park, MD 20742, USA
/
Phone:
301.405.1000
Copyright
© 2024 University of Maryland
/
Privacy /
Web Accessibility
Contact us
with comments, questions and feedback
/