EC ENGR M146

Introduction to Machine Learning

Electrical and Computer Engineering · 4 units · Undergraduate upper division (100-199)

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(Same as Computer Science 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.

Requisites

Official UCLA wording
Requisites: course 131A or Civil and Environmental Engineering 110 or Mathematics 170A or 170E or Statistics 100A; Computer Science 32 or Program in Computing 10C; Mathematics 33A.

BruinTree reads · Prerequisite

confidence 1.00 · from text

all of

Requires

Everything that has to come before this course, not just the courses named in the requisite above.

EC ENGR M146

EC ENGR 131AProbability and Statistics
- MATH 32BCalculus of Several Variables
  - MATH 31BIntegration and Infinite Seriesanother path to it
  - MATH 32ACalculus of Several Variablesanother path to it
- MATH 33BDifferential Equations

Unlocks

What this course is a requisite for, and what those courses lead to in turn.

EC ENGR M146

EC ENGR C147ANeural Networks and Deep Learning
- EC ENGR C147BNeural Networks and Deep Learning II
- EC ENGR 201CArtificial Intelligence on Chip
EC ENGR C247ANeural Networks and Deep Learning
- EC ENGR C247BNeural Networks and Deep Learning II