Selected AI-related Courses

BIO 592-118 
Machine Learning in Biomedicine Research
Graduate
Instructor: Amanda Zhou, Biological Sciences 

DSA 595
Graduate Special Topics in Data Science – Bayesian Computations for Machine Learning
Graduate
Instructor: Jonathan Williams, Statistics
Covers Bayesian methods and computational techniques used in modern machine learning applications.

DSA 595-002 
Generative AI for Science
Graduate
Instructor: Paul Liu, MEAS

GN 428
Introduction to Machine Learning in Biology
Undergraduate
Instructor: Amanda Zhou, Biological Sciences 

MA 591-003
Introduction to Mathematical Theory and Practice of Scientific Machine Learning
Graduate
Instructor: Yeonjong Shin, Mathematics

MEA 591
Machine Learning for Geoscientists
Graduate
Instructor: Del Bohnenstiehl, MEAS

ST 563
Introduction to Statistical Learning
Graduate
Instructors (rotating): Jungeum Kim, Justin Post, Srijan Sengupta, Statistics
This course introduces supervised and unsupervised learning methods used in machine learning, including regularized regression, tree-based methods, ensemble learning and clustering.

ST 554
Analysis of Big Data
Graduate
Instructor: Justin Post, Statistics
Covers statistical methods and computing techniques for large-scale data, including distributed computing frameworks and scalable algorithms.

ST 590-001
Special Topics – Data Visualization
Graduate
Instructor: Dan Harris, Statistics
Focuses on principles and techniques for effective data visualization, exploratory analysis and communication using modern tools and software