Theoretical Foundations of Machine Learning covers advanced mathematical theory including statistical learning theory, Vapnik-Chevronenkis Theory, model selection, high-dimensional models, nonparametric methods, probabilistic analysis, and.
Recent recorded grades — Fall 2023: 3.86 GPA, 100.0% A/AB (n=22 letter grades); Fall 2024: 3.86 GPA, 95.2% A/AB (n=21 letter grades); Fall 2025: 3.69 GPA, 83.3% A/AB (n=24 letter grades).
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E C E/COMP SCI 761 or E C E 830
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Raw average: 3.07/5 from 27 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.
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| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 0 / 30 | 0 |
| LEC 001 | Classroom Instruction | 0 / 30 | 0 |
| LEC 001 | Classroom Instruction | 0 / 30 | 0 |
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Recent recorded grades — Fall 2023: 3.86 GPA, 100.0% A/AB (n=22 letter grades); Fall 2024: 3.86 GPA, 95.2% A/AB (n=21 letter grades); Fall 2025: 3.69 GPA, 83.3% A/AB (n=24 letter grades).
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Latest available · Fall 2025— not enough history to project Spring 2027.
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Descriptions, prerequisites, and recorded course offerings.
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Theoretical Foundations of Machine Learning
Recorded 2026-10-14Theoretical Foundations of Machine Learning
Recorded 2026-10-14Theoretical Foundations of Machine Learning
Recorded 2026-10-14[
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]Original comments behind the course and instructor summaries.
No records available.
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Recorded model configuration, reasoning, and tool conversations.
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