COM SCI 265A

Machine Learning

Computer Science · 4 units · Graduate courses (200-299)

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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.

Requisites

Official UCLA wording
Requisites: courses 263A, 264A.

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Requires

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 111Operating Systems Principles1 more beneath
  - COM SCI 131Programming Languagesanother path to it
  - ENGR 183EWEngineering and Society3 more beneath
  - ENGR 185EWArt of Engineering Endeavors3 more beneath
COM SCI 264AAutomated Reasoning: Theory and Applications
- COM SCI 161Fundamentals of Artificial Intelligence
  - COM SCI 180Introduction to Algorithms and Complexity2 more beneath

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.

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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.