Recent recorded grades — Spring 2023: 3.44 GPA, 65.9% A/AB (n=82 letter grades); Spring 2024: 3.34 GPA, 62.7% A/AB (n=51 letter grades); Spring 2026: 3.17 GPA, 47.6% A/AB (n=21 letter grades). Includes jointly taught sections.
Spring 2027
Statistical Learning
Statistical Learning covers complexity theory, Hilbert spaces, Gaussian processes, Variational Analysis, and concentration inequalities for prediction and decision making.
offering recorded3 credits
Recorded instructors · Spring 2027 Nicolas Garcia Trillos
Summary
1 / 3
Recent recorded grades — Spring 2025: 3.43 GPA, 69.6% A/AB (n=23 letter grades); Fall 2025: 3.42 GPA, 77.8% A/AB (n=18 letter grades); Spring 2026: 3.17 GPA, 47.6% A/AB (n=21 letter grades).
What you’ll be able to do
Demonstrate understanding of statistical theories, methodologies, and applications as tools in scientific inquiries.
Select and utilize the most appropriate statistical methodologies and practices.
Synthesize information pertaining to questions in empirical studies.
Communicate data concepts and analysis results clearly.
Recognize and apply principles of ethical and professional conduct.
Demonstrate knowledge of theoretical properties of many procedures used in machine learning for the purposes of classification, regression, and beyond.
Demonstrate knowledge of classical and modern notions in statistical learning theory including concentration inequalities, measures of statistical complexity, kernel methods for learning, Gaussian processes, and basics of variational inference.
Source: UW Guide
Grade history
↗ average GPA
349349 letter grades
A56.4%56.4%
AB17.5%17.5%
B15.2%15.2%
BC6.0%6.0%
C3.7%3.7%
D0.6%0.6%
F0.6%0.6%
All recorded terms · compare terms & instructors
Prerequisites
Course mapDeclared in Statistics: Statistics and Data Science MS, Data Science MS, Data Engineering MS, or Statistics VISP
- Declared in Statistics: Statistics and Data Science MS, Data Science MS, Data Engineering MS, or Statistics VISP
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Declared in Statistics: Statistics and Data Science MS, Data Science MS, Data Engineering MS, or Statistics VISP
Professors
Spring 2027Historical instructors & teaching patterns
NICOLAS GARCIA TRILLOS is recorded teaching in Spring 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 0 / 48 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
Recent recorded grades — Spring 2025: 3.43 GPA, 69.6% A/AB (n=23 letter grades); Fall 2025: 3.42 GPA, 77.8% A/AB (n=18 letter grades); Spring 2026: 3.17 GPA, 47.6% A/AB (n=21 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Complexity theory, Hilbert spaces, Gaussian processes, Variational Analysis, and concentration inequalities.
- Classical and modern statistical learning theory, kernel methods, and variational inference.
Skills
- Knowledge of statistical learning theory concepts including concentration inequalities, complexity measures, kernel methods, Gaussian processes, and variational inference.
- Understanding theoretical properties of machine learning procedures for classification and regression.
Grades
Latest available · Spring 2026— not enough history to project Spring 2027.
average GPA
A / AB grades
letter grades
Instructor
Grade distribution · % of letter grades
A38.1%38.1%
AB9.5%9.5%
B28.6%28.6%
BC14.3%14.3%
C4.8%4.8%
D0.0%0.0%
F4.8%4.8%
Grades over time
Through Spring 2026
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
UW–Madison
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
Statistical Learning
Recorded 2026-10-14Raw records
[
{
"run_id": "20261014T203208-0851c9a1",
"semester": "1274",
"observed_at": "2026-10-14 17:50:09.728220+00:00",
"offering_id": "1274:932:025356",
"course_id": "STAT 615",
"course_uid": "course_a5f4446c2434cfcb1b7e6969",
"term_id": "1274",
"source_course_id": "025356",
"source_subject_id": "932",
"title": "Statistical Learning",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Not Applicable"
}
]Rate My Professors
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
Madgrades
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesDataset & model
View the dataset on Hugging Face Recorded 2026-10-14
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "6a4e531c98eded3f3eb9d4a85d303554280619eb20af0dc8885b488d8acd07c0",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}