Courses - Spring 2027
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 core concepts in statistical modeling and machine learning through applications in genomics. Students will learn fundamental approaches including hidden Markov models, neural networks, and transformers, whileexploring how these techniques are applied to biological predictionproblems. Topics include protein family prediction,RNA secondary structure prediction, protein structure prediction, and protein languagemodels. Particular emphasis will be placed on the full machine learning workflow, including data curation, model training, and evaluation using both general and domain-specific metrics. No prior knowledge of biology is required.
Covers core concepts in statistical modeling and machine learning through applications in genomics. Students will learn fundamental approaches including hidden Markov models, neural networks, and transformers, whileexploring how these techniques are applied to biological predictionproblems. Topics include protein family prediction,RNA secondary structure prediction, protein structure prediction, and protein languagemodels. Particular emphasis will be placed on the full machine learning workflow, including data curation, model training, and evaluation using both general and domain-specific metrics. No prior knowledge of biology is required.
Flagship Institution of the University System of Maryland
University of Maryland, College Park, MD 20742, USA
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Phone:
301.405.1000
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