COM SCI 260C · CS 260CDeep Learning
Computer Science · 4 units · Graduate courses (200-299)
Not open to students with credit for Electrical and Computer Engineering C147 or C247. Study of basics of deep neural networks and their applications, including but not limited to computer vision, natural language processing, and graph mining. Covers topics including foundation of deep learning, how to train neural network (optimization), architecture designs for various tasks, and other advanced topics. By course end, students are expected to be familiar with deep learning and be able to apply deep learning algorithms to variety of tasks.
Letter grading.
When it runs
- Spring 2026
- Spring 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 180, 260.
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 260C
- COM SCI 180Introduction to Algorithms and Complexity
- COM SCI 32Introduction to Computer Science II
- COM SCI 31Introduction to Computer Science I
- MATH 61Introduction to Discrete Structures
- MATH 31BIntegration and Infinite Series
- COM SCI 260Machine Learning Algorithms
2 direct requisites. Showing 8 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.