Quarter Offered:
Fall

Course Description:

Hands-on experience with computational tools for multi-scale data analysis using example data sets obtained from multiscale biology research. Course will be divided into unsupervised and supervised machine learning. Dr. Valdez-Jasso will teach the mathematical and statistical foundation, programming foundation, and unsupervised machine learning such as regression, independent component analysis, and principal component analysis. Drs. Smarr and Schöneberg will teach supervised machine learning with biomedical applications – such as time series (Smarr) and spatial (Schöneberg) biological data. This will include neural networks and deep learning.

Prerequisite(s):
PhD student
Consent of instructor