An introductory course to topics in machine learning studied from a probability theory viewpoint. Covers an overview of basic probability, inference and estimation, regression algorithms, Markov chains, inference for Markov models and the EM algorithm, Markov decision process, and reinforcement learning. In addition to covering mathematical and algorithmic details, the course includes several hands-on projects to implement the machine learning algorithms.

Prerequisites: ECE 139 or PSTAT 120A or equivalent.

4

Units

Letter

Grading

1, 2, 3

Passtime

None

Level Limit

Engineering

College
PEDARSANI R
No info found

Lecture

GIRV 2112
M W
15:30 PM - 16:45 PM
29 / 30 Closed

Sections

ILP 3105
F
10:00 AM - 10:50 AM
29 / 30
ECE 181
40 / 50 Enrolled
Introduction to Computer Vision
Yuan-Fang Wang 1.9
T R
12:30 PM - 13:45 PM
42.3% A
ECE 188B
60 / 85 Enrolled
Senior Electrical Engineering Project
Ilan Ben-Yaacov 3.9
M W
12:30 PM - 13:45 PM
91.3% A
ECE 189B
31 / 60 Enrolled
Senior Computer Systems Project
Yogananda Isukapalli 4.9
M W
09:30 AM - 10:45 AM
84.4% A
ECE 192
0 / 10 Enrolled
Projects in Electrical and Computer Engineering
T B A
100.0% A
ECE 193
0 / 10 Enrolled
Internship in Industry
T B A
87.5% A
ECE 194BB
14 / 25 Enrolled
Computer Engineering
Cheng-Zhong Qin 2.5
M W
11:00 AM - 12:15 PM
88.3% A