COMP 565 Machine Learning in Genomics and Healthcare (4 credits)

Overview

Computer Science (Sci) : 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.

Terms: Fall 2024

Instructors: Li, Yue (Fall)

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

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