COM SCI 265A · CS 265AMachine Learning
Computer Science · 4 units · Graduate courses (200-299)
Introduction to machine learning. Learning by analogy, inductive learning, modeling creativity, learning by experience, role of episodic memory organization in learning. Examination of BACON, AM, Eurisko, HACKER, teachable production systems. Failure-driven 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: courses 263A, 264A.
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 265A
- COM SCI 263ALanguage and Thought
- COM SCI 130Software Engineering
- COM SCI 131Programming Languages
- COM SCI 264AAutomated Reasoning: Theory and Applications
- COM SCI 161Fundamentals of Artificial Intelligence
2 direct requisites. Showing 13 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.
No course in the catalog lists this as a requisite.