Statistical Machine Learning is used to discover patterns and relationships in large data sets. Topics will include: data exploration, classification and regression tress, random forests, clustering and association rules. Building predictive models focusing on model selection, model comparison and performance evaluation. Emphasis will be on concepts, methods and data analysis; and students are expected to complete a significant class project, individual or team based, using real-world data.

Prerequisites: PSTAT 120A-B and PSTAT 126 with a minimum grade of C or better.

4

Units

Optional

Grading

1, 2

Passtime

None

Level Limit

Letters and science

College
Unlocks PSTAT 135 PSTAT 134 PSTAT 234
These majors only finms stsds actsc stsap stats
YU G H
Guo Yu
3.1
22 reviews

Lecture

BUCHN1930
T R
12:30 PM - 13:45 PM
80 / 80 Full

Sections

ILP 4101
M
15:00 PM - 15:50 PM
20 / 20 Full
GIRV 2119
M
16:00 PM - 16:50 PM
20 / 20 Full
ILP 3209
M
17:00 PM - 17:50 PM
20 / 20 Full
ILP 3209
M
14:00 PM - 14:50 PM
20 / 20 Full
See All
Winter 2024 . Coburn K M
ILP 1101
T R
14:00 PM - 15:15 PM
Winter 2024 . Coburn K M
NH 1006
T R
17:00 PM - 18:15 PM
See All
PSTAT 131 Yu G Fall 2023 Total: 62
PSTAT 131 Yu G Fall 2022 Total: 75
See All
22
3.1
PSTAT131 . Yu G H 26 Days Ago

I would not recommend this professor unless you have no other choice. The lectures were disorganized, and it felt like they were just reading off the slides without offering any deeper explanations. When students asked questions, the responses were often vague or dismissive, which made it hard to clarify important concepts.

0 helpful 0 unhelpful
PSTAT131 . Yu G H 1 Year, 10 Days Ago

Avoid this guy if you can! He gave a super difficult final exam, with a lot of confusing multiple choice/true or false questions. He was difficult to reach outside of the class. His office hour didn't have anyone, including himself.

0 helpful 0 unhelpful
PSTAT131 . Yu G H 1 Year, 13 Days Ago

Very difficult to get a hold of outside of one or two days a week. Always rushing out and prefer to hide behind emails or zoom for communications. Unwilling to set up office hour outside of days he lectures. Don't feel like he cares about student's education, but cares more about his own time and schedule.

0 helpful 0 unhelpful
PSTAT131 . Yu G H 2 Years Ago

Class is graded on Midterm/Take Home Final/4 HW/4 Quiz. Midterm content was fair and was primarily lecture material. Midterm had multiple choice and free response. Homework was reasonable and was based off lab assignment topics and code. You could work with a partner on the homework and the final. Overall good instructor. would take again

0 helpful 0 unhelpful
PSTAT131 . Yu G H 3 Years Ago

Lectures are solid and he really tries his best to simplify down complex ideas to make them understandable. Homework is more like follow along learn as you go rather than challenging to complete, and really helps with understanding models. Lectures can be a little math heavy but he doesn't expect you to memorize, instead focuses on concepts

0 helpful 0 unhelpful
PSTAT131 . Yu G H 3 Years Ago

Hes so awful omg don't take him

0 helpful 1 unhelpful
See all 22 reviews
See All
PSTAT 131
80 / 80 Full
Introduction to Statistical Machine Learning
Guo Yu 3.1
T R
11:00 AM - 12:15 PM
58.1% A
PSTAT 122
98 / 100 Enrolled
Design and Analysis of Experiments
Peter Chi 4.9
M W
09:30 AM - 10:45 AM
45.4% A
PSTAT 126
98 / 100 Enrolled
Regression Analysis
Puja Pandey 4.3
T R
17:00 PM - 18:15 PM
40.0% A
PSTAT 126
86 / 100 Enrolled
Regression Analysis
Yuedong Wang 3.1
M W
12:30 PM - 13:45 PM
40.0% A
PSTAT 126
46 / 75 Enrolled
Regression Analysis
Ali Abuzaid 2.1
T R
11:00 AM - 12:15 PM
40.0% A
PSTAT 130
125 / 125 Full
SAS Base Programming
Julie Swenson 4.3
T
09:30 AM - 10:45 AM
36.1% A
PSTAT 134
91 / 100 Enrolled
Statistical Data Science
Katie Coburn 3.4
T R
12:30 PM - 13:45 PM
49.6% A