STATS M231A
Pattern Recognition and Machine Learning
Statistics · 4 units · Graduate courses (200-299)
(Same as Computer Science M276A.) Designed for graduate students. Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM, boosting.
S/U or letter grading.
Requisites
UCLA lists no requisites for this course.
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
Nothing — this is an entry point.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
STATS M231A
- STATS 213Synthetic Data Generation
- STATS 231BMethods of Machine Learning
- STATS 231CTheories of Machine Learning
- STATS C263Generative Data Science
3 courses list this as a requisite. The whole downstream is here — 6 courses over 2 levels. Every course here opens its own tree.