ECE-480
APPLIED PROB FOR STAT LEARNING
Not in Fall 2026
Term
Overview
Feedback is mixed. The clearest upside is that teaching clarity stands out. The ratings are softer than the more upbeat comments might suggest. Best for students who want a structured class rather than chaos.
DepartmentELEC&CMP
Terms offeredFall
Typical enrollment35–43
Semesters of data2
5.3
Hrs / week
50
Responses
78
Enrollment
64%
Response Rate
Evaluation Scores
Overall quality
Teaching, content, and experience combined.
3.5
Intellectually stimulating
Challenges students to think deeply.
3.7
Instructor effectiveness
Explains concepts and facilitates learning.
3.7
Difficulty
Higher means harder.
3.1
Feedback Analysis
Feedback Analysismedium
Analysis based on student evaluations
Based on 150 comments across 2 sections
Feedback is mixed. The clearest upside is that teaching clarity stands out. The ratings are softer than the more upbeat comments might suggest. Best for students who want a structured class rather than chaos.
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
Lighter lecture burden
Student comments describe this as more discussion-, seminar-, or workshop-driven than lecture-dependent. The lecture burden itself does not sound like the main source of friction.
Strengths
• Teaching clarity stands out; students repeatedly say the material is explained clearly and effectively.
Tradeoffs
• There is no single dominant complaint theme, but the feedback is not uniformly glowing either.
Best fit for
Best for students who want a structured class rather than chaos.
Watch out for
• A large share of the evidence comes from one instructor's version of the course, so this may not generalize cleanly.
Student Responses
I learnt how machine learning works from a probabilisitc stand point.
Fall 2024 · Tantum, Stacy
I learned how to model data with GMMs, the nuances of the bias-variance trade off in machine learning, and how to perform machine learning on small datasets in python.
Fall 2024 · Tantum, Stacy
Foundational math for ML with a bayesian approach to understanding probability, supervised learning (knn) and unsupervised learning (kmeans and GMMs).
Fall 2024 · Tantum, Stacy
I learned statistics behind machine learning. Also learned various ways to analyze data and likelihood
Fall 2024 · Tantum, Stacy
We went through some techniques of Bayesian machine learning (Bayesian updating, gaussian mixture models, etc)
Fall 2024 · Tantum, Stacy
Rating History
Rating history
Error bars show \u00B11 std dev
| Term | Instructor | Overall | Difficulty | Hrs/wk | Enrolled |
|---|---|---|---|---|---|
| Fall 2024 | Tantum, Stacy 3.5Rate My ProfessorsQuality3.5Difficulty3.0Would retake55%Based on 41 ratingsClick to view on RMP → | 3.6 | 3.1 | 5.2 | 43 |
| Fall 2023 | Tantum, Stacy 3.5Rate My ProfessorsQuality3.5Difficulty3.0Would retake55%Based on 41 ratingsClick to view on RMP → | 3.3 | 3.1 | 5.5 | 35 |
Instructor
Tantum, StacyELEC&CMP
Also teaches
ECE-487 SYS DESIGN FOR ML & SIG PROCES3.2ECE-580 INTRO TO MACHINE LEARNING4.2EGR-101L ENGR DESIGN & COMMUNICATION3.8