Deep learning predictive models, optimization, and sampling methods. Emphasis is on learning the probabilistic framework, deriving efficient estimators using matrix algebra, and developing scalable artificial intelligence algorithms and code to solve real-world research problems.

Prerequisites: PSTAT 120A-B-C; consent of instructor.

1 - 6

Units

Optional

Grading

1, 2, 3

Passtime

None

Level Limit

Letters and science

College
GU M
No info found
ILP 4103
T
16:00 PM - 16:50 PM
6 / 30

PSTAT 262DS
7 / 30 Enrolled
Seminars In Probability and Statistics
Mengyang Michael Gu 3.9
M W
11:00 AM - 12:15 PM
PSTAT 262FM
5 / 20 Enrolled
Seminars In Probability And Statistics
Jean-Pierre Fouque 4.0
F
10:00 AM - 11:50 AM
PSTAT 263
3 / 75 Enrolled
Research Seminars in Probability and Statistics
Wendy Meiring 2.8
M W
15:30 PM - 16:45 PM
PSTATW 274
3 / 30 Enrolled
Time Series
Feldman R
Async online
PSTAT 275
12 / 30 Enrolled
Survival Analysis
Yuedong Wang 3.4
M W
17:00 PM - 18:15 PM
PSTAT 277A
3 / 30 Enrolled
Advanced Time Series
Peters G W
T R
18:30 PM - 19:45 PM