Fall 2025 Computer Science E4762 section V01

Machine Learning for Functional Genomics

ML for Functional Genomic

Call Number 20354
Points 3
Grading Mode Standard
Approvals Required None
Instructor David A Knowles
Type LECTURE
Method of Instruction On-Line Only
Course Description

This course will introduce modern probabilistic machine learning methods using applications in data analysis tasks from functional genomics, where massively-parallel sequencing is used  to measure the state of cells: e.g. what genes are being expressed, what regions of DNA (“chromatin”) are active (“open”) or bound by specific proteins. 

Web Site Vergil
Department Video Network
Enrollment 2 students (99 max) as of 5:06PM Saturday, October 18, 2025
Subject Computer Science
Number E4762
Section V01
Division School of Engineering and Applied Science: Graduate
Open To Engineering:Graduate
Fee $395 CVN Course Fee
Note VIDEO NETWORK STUDENTS ONLY
Section key 20253COMS4762WV01