STATS 201B · STAT 201BStatistical Modeling and Learning
Statistics · 4 units · Graduate courses (200-299)
Methods of model fitting and parameter estimation, with emphasis on regression and classification techniques, including those from machine learning. Interest in either obtaining suitable conditional expectation function or estimating meaningful parameters of underlying probabilistic model to make inferences or predictions from data. Focus on what is to be done when linear models are not appropriate and may produce misleading estimates. Coverage of classical must know model fitting and parameter estimation techniques such as maximum likelihood fitting of generalized linear models. Exploration of broader regression/classification techniques that have been ubiquitous in machine learning literature, with special attention to regularization and kernelized methods.
S/U or letter grading.
When it runs
- Winter 2026
- Winter 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
Requisites: courses 200A, 201A.
BruinTree reads · Prerequisite
confidence 1.00 · from textRequires
Everything that has to come before this course, not just the courses named in the requisite above.
STATS 201B
- STATS 200AApplied Probability
- STATS 100AIntroduction to Probability
- MATH 170AProbability Theory I
- STATS 201AResearch Design, Sampling, and Analysis
2 direct requisites. Showing 9 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
STATS 201B
- STATS 201CAdvanced Modeling and Inference
- STATS 206Modern Survey Methods
- STATS 211Topics in Economics and Machine Learning
- STATS 213Synthetic Data Generation
- STATS C263Generative Data Science
5 courses list this as a requisite. The whole downstream is here — 5 courses over 1 level. Every course here opens its own tree.