Subject: Data Science
Course Number: 226
Course Title: Bayesian Methods and Gaussian Processes
Min units: 2      Max units: 3
School: Graduate Division
Department: Health Data Science Program

Course Description: This course provides an introduction to Bayesian statistics, Markov Chain Monte Carlo (MCMC) sampling, and Gaussian Processes. The first two units cover the fundamentals of Bayesian methods and MCMC, and the final optional unit explores Gaussian processes. Students will gain practical skills in applying these techniques to real-world problems using R, STAN, and JAGS.
Prerequisites: Basic knowledge of probability and statistics (BIOSTAT 200 and BIOSTAT 208 equivalent); programming skills in R (BIOSTAT 213 and BIOSTAT 214 equivalent); some familiarity with calculus and linear algebra (especially for the extra Gaussian processes unit).
Restrictions: This course is part of the Health Data Science Masters and Certificate Program and may have space limitations. Auditing is not permitted.
Activities: Direct - Lecture, Direct - Project, Direct - Discussion, Student - Lecture

Instructor of Record: J. Kornak
May the student choose the instructor for this course? No
Does enrollment in this course require instructor approval? No

Quarter(s) Offered: Fall
Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
Graduate Division course: Yes
Is this a web-based online course? No
Is this an Interprofessional Education (IPE) course? No
May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
Repeat course for credit? No