COMP 565 — Machine Learning in Genomics and Healthcare

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.

• Credits: 4
• Faculty: Faculty of Science
• Department: Computer Science
• Taught by: Yue Li
• Prerequisites: (BIOL 202 or BIOL 302) and MATH 324 and (COMP 451 or COMP 551), or equivalents.

Sections offered

• Section 001 (Lec), Mon Wed 11:35 am-12:55 pm — 0 seats open
• Section 002 (Lec), Mon Wed 11:35 am-12:55 pm — 0 seats open