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 134 PSTAT 135 PSTAT 234 PSTAT 235
These majors only finms stsds actsc stsap stats
YU G H
Guo Yu
2.9
27 reviews
ILP 4101
M
15:00 PM - 15:50 PM
19 / 20

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

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Fall 2025 . Yu G
ILP 2302
M W
11:00 AM - 12:15 PM
Fall 2025 . Yu G
ILP 2302
M W
12:30 PM - 13:45 PM
See All
PSTAT 131 Yu G Fall 2023 Total: 62
PSTAT 131 Yu G Fall 2022 Total: 75
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27
2.9
PSTAT131 . Yu G H 6 Days Ago

Terrible professor. Lectures are disorganized and confusing, and he rarely answers questions clearly. He reads straight from the slides without explaining concepts. Grading is inconsistent, and there's no feedback on assignments. Office hours feel rushed and unhelpful. I learned more from YouTube than from class.

0 helpful 0 unhelpful
PSTAT131 . Yu G H 27 Days Ago

Don't know what's up with the negative reviews. I agree that lectures were focused on theoretical concepts it's math course so what do you expect? He knows the material inside out, with well-structured homeworks that actually help you understand the content. Final exam was fair, it's just that without a midterm we didn't know what was expected.

0 helpful 0 unhelpful
PSTAT131 . Yu G H 29 Days Ago

Lectures were pretty useless, though he does know his stuff. Final exam was poorly written, class average of 50% (though he did curve the final grades). Provided 4 bullet points for instructions on the final project.

0 helpful 0 unhelpful
PSTAT131 . Yu G H A Month Ago

Guo seems friendly and approachable at first, as well as knowledgeable in machine learning concepts, but his teaching style doesn't convey that much. Sections seemed practically useless, lectures focused too heavily on theory with little to no explanations, and course expectations, like for the final project, were outlined weakly and much too late.

0 helpful 0 unhelpful
PSTAT131 . Yu G H A Month Ago

do not take.

0 helpful 0 unhelpful
PSTAT131 . Yu G H 5 Months 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.

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See all 27 reviews
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PSTAT 131
80 / 80 Full
Introduction to Statistical Machine Learning
Guo Yu 2.9
T R
11:00 AM - 12:15 PM
58.1% A
PSTAT 122
100 / 100 Full
Design and Analysis of Experiments
Peter Chi 4.9
M W
09:30 AM - 10:45 AM
45.4% A
PSTAT 126
82 / 100 Enrolled
Regression Analysis
Yuedong Wang 3.4
M W
12:30 PM - 13:45 PM
40.0% A
PSTAT 126
41 / 75 Enrolled
Regression Analysis
Ali Abuzaid 3.6
T R
11:00 AM - 12:15 PM
40.0% A
PSTAT 126
99 / 100 Enrolled
Regression Analysis
Puja Pandey 4.3
T R
17:00 PM - 18:15 PM
40.0% A
PSTAT 130
122 / 125 Enrolled
SAS Base Programming
Julie Swenson 4.3
T
09:30 AM - 10:45 AM
36.1% A
PSTAT 134
90 / 100 Enrolled
Statistical Data Science
Katie Coburn 3.3
T R
12:30 PM - 13:45 PM
49.6% A