COMP 565 Machine Learning in Genomics and Healthcare

4 credits · Computer Science · Faculty of Science · Offered: Fall

Linear models in statistical genetics, causal inference, single-cell genomics, multi-omic learning, electronic health record mining. Applications of machine learning techniques: linear regression, latent factor models, variational Bayesian inference, neural networks, model interpretation.

Prerequisites

(BIOL 202 or BIOL 302) and MATH 324 and (COMP 451 or COMP 551), or equivalents.

Prerequisite courses: BIOL 202, BIOL 302, COMP 451, COMP 551, MATH 324

Restrictions

Not open to students who have taken COMP 597 or COMP 598 when the topic was "Machine Learning in Genomics and Healthcare".

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