ECE-580
INTRO TO MACHINE LEARNING
Not in Fall 2026
Term
Instructor
Overview
Feedback is mostly positive. The strongest signal is that students generally rate the course well. Best for students who are genuinely interested in the topic and willing to engage with the course on its own terms.
DepartmentELEC&CMP
Terms offeredSpring
Typical enrollment22–36
Semesters of data2
6.8
Hrs / week
49
Responses
88
Enrollment
56%
Response Rate
Evaluation Scores
Overall quality
Teaching, content, and experience combined.
4.3
Intellectually stimulating
Challenges students to think deeply.
4.3
Instructor effectiveness
Explains concepts and facilitates learning.
4.4
Difficulty
Higher means harder.
3.5
Feedback Analysis
Feedback Analysismedium
Analysis based on student evaluations
Based on 61 comments across 3 sections
Feedback is mostly positive. The strongest signal is that students generally rate the course well. Best for students who are genuinely interested in the topic and willing to engage with the course on its own terms.
Student Reports
How hard is the A?
A is doable but not automatic
The signal here is more do-the-work-and-you-should-be-fine than easy-A chatter. Students do not describe the A as automatic, but the evidence also does not paint grading as punishing.
Homework Load
Moderate homework load
Homework load looks moderate. The recurring signal is steady weekly work, but not a course that turns every assignment into a grind.
Lecture Load
Regular lecture load
Lectures matter here, but the evidence points to a fairly standard lecture burden rather than a course dominated by long or exceptionally dense lectures.
Strengths
• Instructor ratings are strong even when the comments do not cluster around one obvious positive theme.
Tradeoffs
• There is no single dominant complaint theme, but the feedback is not uniformly glowing either.
Best fit for
Best for students who are genuinely interested in the topic and willing to engage with the course on its own terms.
Student Responses
Through this class, I was able to learn a plethora of contemporary ML techniques, both from theoretical and practical standpoints. The course also helped me think deeply and technically about the ML topics we learned, as well as the societal implications of the use of these techniques. Lastly, we also learned about how to talk and present ML-related work, which is as important as doing the work itself.
Spring 2025 · Tantum, Stacy
Introduction to ML models and evaluation methods, like SVMs, Trees and ROCs.
Spring 2025 · Tantum, Stacy
I learned various machine learning techniques, how they are deployed in industry, and how to implement them myself.
Spring 2025 · Tantum, Stacy
I learned about classifiers, and SVMs. I learned how to code better in Python while completing the homework assignments. I also learned about regression, specifically LASSO.
Spring 2025 · Tantum, Stacy
learned a lot of differnet machine learning algoirthms and different cases when you should use certain things and the tradeofffs
Spring 2025 · Tantum, Stacy
Rating History
Rating history
Error bars show \u00B11 std dev
| Term | Instructor | Overall | Difficulty | Hrs/wk | Enrolled |
|---|---|---|---|---|---|
| Spring 2025 | Tantum, Stacy 3.5Rate My ProfessorsQuality3.5Difficulty3.0Would retake55%Based on 41 ratingsClick to view on RMP → | 4.3 | 3.5 | 6.5 | 58 |
| Spring 2024 | Tantum, Stacy 3.5Rate My ProfessorsQuality3.5Difficulty3.0Would retake55%Based on 41 ratingsClick to view on RMP → | — | — | 7.5 | 30 |
Instructor
Tantum, StacyELEC&CMP
Also teaches
ECE-480 APPLIED PROB FOR STAT LEARNING3.5ECE-487 SYS DESIGN FOR ML & SIG PROCES3.2EGR-101L ENGR DESIGN & COMMUNICATION3.8