MATRIX METHODS IN MACHINE LEARNING covers linear algebraic foundations and matrix methods for machine learning applications like classification, clustering, and data analysis.
Reviewers describe the workload as excessive, with heavy weekly homework and four exams. The course is considered difficult, featuring hard proofs and exams that are hard to finish.
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(MATH 234, 320, 340, 341, or 375) and (E C E 203,COMP SCI 200, 220, 300, 301, 302, 310, 320, or placement into COMP SCI 300), graduate/professional standing, or declared in Capstone Certificate in Computer Sciences for Professionals
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Grigoris Chrysos receives uniformly negative reviews for poor organization and unclear instruction. Students report that lectures rely on unrelated videos and slide reading, while exams are excessively difficult with complex proofs. Grading criteria are opaque, and the workload is described as overwhelming and poorly structured.
Recent recorded grades — Fall 2024: 3.46 GPA, 70.8% A/AB (n=48 letter grades); Fall 2025: 3.32 GPA, 68.9% A/AB (n=74 letter grades).
Grigoris Chrysos is the current instructor, though no specific reviews are provided for his teaching. Historical reviews for other instructors highlight a course with significant workload and challenging exams. Students report mixed experiences, ranging from supportive environments to confusing explanations and frustrating grading practices.
EDUARDO ROMERO ARVELO is recorded teaching in Fall 2021, Fall 2023, Spring 2024, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.
GRIGORIS CHRYSOS is recorded teaching in Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.
MATTHEW MALLOY is recorded teaching in Fall 2019, Spring 2020, Spring 2022. Recorded history may be incomplete and does not establish a future schedule.
RAMYA KORLAKAI VINAYAK is recorded teaching in Fall 2020, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.
ROBERT NOWAK is recorded teaching in Fall 2014, Fall 2015, Fall 2020, Fall 2021, Fall 2022, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.
YU HU is recorded teaching in Spring 2020, Fall 2023. Recorded history may be incomplete and does not establish a future schedule.
Recorded history may be incomplete and does not establish a future schedule.
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| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 002 | Classroom Instruction | 78 / 130 | 0 |
| LEC 002 | Classroom Instruction | 15 / 130 | 0 |
| LEC 002 | Classroom Instruction | 4 / 130 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Grigoris Chrysos receives uniformly negative reviews for poor organization, unclear grading, and excessive difficulty. Students report the course feels disorganized and overly demanding.
Recent recorded grades — Spring 2025: 3.06 GPA, 36.1% A/AB (n=108 letter grades); Fall 2025: 3.43 GPA, 70.5% A/AB (n=207 letter grades); Spring 2026: 3.34 GPA, 65.8% A/AB (n=149 letter grades).
Reviewers describe the workload as excessive, with heavy weekly homework and four exams. The course is considered difficult, featuring hard proofs and exams that are hard to finish.
Students find the teaching approach frustrating, citing reliance on YouTube videos and unclear lecture content. Grading criteria are reported as opaque, with grades withheld until the semester ends.
– average GPA
Approximate 80% prediction interval
The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.
The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.
8 earlier forecasts · 0.12 GPA average error.
Through Fall 2026
Latest available grades · Spring 2026 · all course levels
GPA
Higher than % of other courses in this group.
Course GPAs · red marks this course’s range
letter grades
More recorded grades than % of other courses in this group.
Typical course in this group: letter grades.
1283 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.
Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.
Descriptions, prerequisites, and recorded course offerings.
Observations at scan time; dates do not imply when a catalog change took effect.
Matrix Methods in Machine Learning
Recorded 2026-09-07Matrix Methods in Machine Learning
Recorded 2026-09-07Matrix Methods in Machine Learning
Recorded 2026-09-07[
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]Original comments behind the course and instructor summaries.
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded model configuration, reasoning, and tool conversations.
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