DS BMED 206Advanced Machine Learning Applications in Biomedicine
Data Science in Biomedicine · 4 units · Graduate courses (200-299)
Statistical models for analysis of biomedical data that captures the structure of the data and accounts for the constraints. Topics include Bayesian models, probabilistic graphical models, deep learning, time series, dynamical systems, stochastic processes, scalable inference (gradient descent, stochastic gradient descent, expectation-maximization, Markov chain Monte Carlo, variational inference), privacy-preserving inference (differential privacy, inference over encrypted data), interpretable machine learning, and fairness and bias.
Letter grading.
When it runs
- Spring 2026
- Spring 2027
Scheduled, not typical — from UCLA’s Schedule of Classes, which publishes Fall 2025 through Spring 2027 and nothing before it.
Requisites
Official UCLA wording
Requisite: course 200 or equivalent.
BruinTree reads · Prerequisite
confidence 1.00 · from UCLA’s structured dataRequires
Everything that has to come before this course, not just the courses named in the requisite above.
DS BMED 206
- DS BMED 200Foundations of Data Science
1 direct requisite. The whole upstream is here — 1 course over 1 level. Every course here opens its own tree.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
No course in the catalog lists this as a requisite.