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 actsc stsds stsap stats
Laura Baracaldo
1.8
24 reviews
PHELP1525
M
13:00 PM - 13:50 PM
19 / 20

PHELP1525
M
11:00 AM - 11:50 AM
19 / 20

PHELP1525
M
12:00 PM - 12:50 PM
17 / 20

PHELP1525
M
10:00 AM - 10:50 AM
17 / 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 Baracaldo Lan Fall 2024 Total: 64
PSTAT 131 Yu G Winter 2025 Total: 156
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24
1.8
PSTAT131 . 6 Months Ago

Prof Baracaldo is not the best at explaining 131 material; she goes rly deep into all of statistical ML proofs and is often times confusing. She's nice though and gives good feedback on project if you approach her after class. HW and quizzes are not bad. TA is rly helpful. Overall not a hard class but ML concepts are hard to understand in general.

0 helpful 0 unhelpful
PSTAT131 . 7 Months Ago

Super nice professor, class was very fairly graded on easy homework and final project, clear grading criteria. Available after class and at office hours to answer any and all questions, will help you directly with any specific problem. Quizzes were very simple and open book/internet, just a basic check to make sure you're paying attention.

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PSTAT100 . 3 Months Ago

She knows a lot but can not express it in understandable way, the lectures were not organized, slides are incomplete because she writes additional notes during lecture. No clue what to focus on before final and midterms and didn't even follow her own syllabus. Take her course if you want to lower you GPA and waste time.

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PSTAT100 . 8 Months Ago

I am the PSTAT 115 student.the best professor I have ever met at UCSB. Well-prepared and organized lecture The exams are not easy, but if you follow her step, you will do well on the exams. she should not got low grade, she is so patient when answer my question.I got 100% on canvas, it gives me confidence and I really love it and the professor

0 helpful 0 unhelpful
pstat126 . 11 Months Ago

4 HW's with 1.5 weeks to finish each, 3 online quizzes bi-weekly. Lectures were so confusing but necessary to succeed so attend all. Midterm and final both took questions from the practice exam and had 50% R output interpretation 30% Derivation/Proof 20% MC/TF and extra credit. Attend sections right before the exam, TA's went over helpful topics.

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pstat134 . 11 Months Ago

If you want to be concerned about whether you will graduate on time due to a single question on a single exam, this is the class to take. Prof never responds to emails/Nectir (Slack).

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PSTAT 131
80 / 80 Full
Introduction to Statistical Machine Learning
T B A
T R
17:00 PM - 18:15 PM
58.3% A
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80 / 100 Enrolled
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Puja Pandey 4.3
T R
17:00 PM - 18:15 PM
42.3% A
PSTAT 127
96 / 125 Enrolled
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Wendy Meiring 2.8
T R
11:00 AM - 12:15 PM
36.2% A
PSTAT 127
77 / 100 Enrolled
Advanced Statistical Models
Wendy Meiring 2.8
T R
11:00 AM - 12:15 PM
36.2% A
PSTAT 130
124 / 125 Enrolled
SAS Base Programming
Julie Swenson 4.3
T
09:30 AM - 10:45 AM
35.8% A
PSTAT 130
25 / 25 Full
SAS Base Programming
T
12:00 PM - 12:50 PM
35.8% A
PSTAT 134
81 / 88 Enrolled
Statistical Data Science
Katie Coburn 3.3
T R
15:30 PM - 16:45 PM
55.5% A