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
21 reviews

Lecture

BUCHN1930
T R
11:00 AM - 12:15 PM
0 / 80

Sections

PHELP2514
M
10:00 AM - 10:50 AM
0 / 20
PHELP2514
M
11:00 AM - 11:50 AM
0 / 20
GIRV 2116
M
12:00 PM - 12:50 PM
0 / 20
ARTS 1353
M
13:00 PM - 13:50 PM
0 / 20
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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
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PSTAT 131 Yu G Fall 2023 Total: 62
PSTAT 131 Yu G Fall 2022 Total: 75
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21
3.1
PSTAT131 . Yu G H 10 Months 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 10 Months 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 2 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
PSTAT131 . Yu G H 3 Years Ago

His first time teaching. He's not understanding and gives you less time to do work when he says he will give you 2 weeks

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