2019 Fall
PHYSICS 188 102 - DIS 102
Bayesian Data Analysis and Machine Learning for Physical Sciences
Current Enrollment
Total Open Seats:
21
Enrolled: 10
Waitlisted: 0
Capacity: 31
Waitlist Max: 10
No Reserved Seats
Hours & Workload
3 hours of instructor presentation of course materials per week, 8 hours of outside work hours per week, and 1 hours of the exchange of opinions or questions on course material per week.
Other classes by Biwei Dai
Course Catalog Description
The course design covers data analysis and machine learning, highlighting their importance to the physical sciences. It covers data analysis with linear and nonlinear regression, logistic regression, and gaussian processes. It covers concepts in machine learning such as unsupervised and supervised regression and classification learning. It develops Bayesian statistics and information theory, covering concepts such as information, entropy, posteriors, MCMC, latent variables, graphical models and hierarchical Bayesian modeling. It covers numerical analysis topics such as integration and ODE, linear algebra, multi-dimensional optimization, and Fourier transforms.
Rules & Requirements
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
No Reserved Seats