COM SCI M146 · CS M146Introduction to Machine Learning
Computer Science · 4 units · Undergraduate upper division (100-199)
(Same as Electrical and Computer Engineering M146.) Introduction to breadth of data science. Foundations for modeling data sources, principles of operation of common tools for data analysis, and application of tools and models to data gathering and analysis. Topics include statistical foundations, regression, classification, kernel methods, clustering, expectation maximization, principal component analysis, decision theory, reinforcement learning and deep learning.
Letter grading.
When it runs
Not on the schedule for any of Fall 2025 through Spring 2027. UCLA publishes only that window, so this does not mean the course is gone — check the official listing.
Requisites
Official UCLA wording
Requisites: course 32 or Program in Computing 10C; Civil and Environmental Engineering 110 or Electrical and Computer Engineering 131A or Mathematics 170A or 170E or Statistics 100A; Mathematics 33A.
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.
COM SCI M146
- COM SCI 32Introduction to Computer Science II
- COM SCI 31Introduction to Computer Science I
- C&EE 110Introduction to Probability and Statistics for Engineers
- MATH 32ACalculus of Several Variables
- COMPTNG 10CAdvanced Programming
- COMPTNG 10BIntermediate Programming
- COMPTNG 10AIntroduction to Programming
- EC ENGR 131AProbability and Statistics
- MATH 33ALinear Algebra and Applications
- STATS 100AIntroduction to Probability
- MATH 170AProbability Theory I
- MATH 131AAnalysis
- MATH 170EIntroduction to Probability and Statistics 1: Probability
8 direct requisites. Showing 33 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.
COM SCI M146
- COM SCI C160FFoundation Models: Principles and Practice
- COM SCI 162Natural Language Processing
- STATS C163Generative Data Science
- COM SCI 163Deep Learning for Computer Vision
- COM SCI 247Advanced Data Mining
- COM SCI C260FFoundation Models: Principles and Practice
- COM SCI 261Deep Generative Models
7 courses list this as a requisite. The whole downstream is here — 8 courses over 2 levels. Every course here opens its own tree.