BME 234Machine Learning and Artificial Intelligence in Genomics

5 creditsGraduateStudent Option

Graduate-level course in machine learning and artificial intelligence methods applied to genomics. Covers hidden Markov models, machine learning approaches for classification and regression, convolutional neural networks, self-supervised language models, and their application to genome annotation, genome-wide association studies, and predicting the molecular and clinical consequences of genetic variation. Prior coursework in linear algebra, probability and statistics, and experience with programming and scientific computing using Python are strongly recommended. Familiarity with basic biology and genomics is helpful but not required.

Prerequisites

Prerequisite(s): BME 230A. Enrollment is restricted to graduate students; undergraduates may enroll by permission of the instructor. Students are assumed to have prior undergraduate-level coursework in linear algebra (e.g., AM 10, MATH 24 or equivalent), probability and statistics (e.g., STAT 131 or equivalent), and basic programming (e.g., CSE 20, AM 129 or equivalent).

Also offered as AM 234.

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When it runs

Spring 2026Open Spring 2026

Section	Type	When	Where	Instructor	Seats
01	Lecture	Tue Thu 5:20pm–6:55pm	R Carson Acad 252	N. Ioannidis	Open 13/35

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