ECE-684
NATURAL LANGUAGE PROCESSING
Offered Fall 2026
Term
Overview
Feedback is mixed. The clearest upside is that some students still find real value in the course. Best for students who are genuinely interested in the topic and willing to engage with the course on its own terms.
DepartmentELEC&CMP
Terms offeredFall
Typical enrollment80–100
Semesters of data3
4.9
Hrs / week
119
Responses
276
Enrollment
43%
Response Rate
Evaluation Scores
Overall quality
Teaching, content, and experience combined.
3.7
Intellectually stimulating
Challenges students to think deeply.
3.7
Instructor effectiveness
Explains concepts and facilitates learning.
3.9
Difficulty
Higher means harder.
2.8
Feedback Analysis
Feedback Analysishigh
Analysis based on student evaluations
Based on 66 comments across 3 sections
Feedback is mixed. The clearest upside is that some students still find real value in the course. 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
• There is some positive signal here, but it is not concentrated around one dominant strength.
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.
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
Variable-length input to deep models.
Fall 2023 · Wang, Patrick
NLP knowledge
Fall 2023 · Wang, Patrick
Programming in Python; Pytorch Framework; Basic NLP knowledge
Fall 2023 · Wang, Patrick
Solving problems involving natural language processing, received an introduction PyTorch, getting to think about encoders/decoders more deeply
Fall 2023 · Wang, Patrick
I learned some general knowledge about ML/DL and statistics, the basics of the NLP.
Fall 2023 · Wang, Patrick
Rating History
Rating history
Error bars show \u00B11 std dev
| Term | Instructor | Overall | Difficulty | Hrs/wk | Enrolled |
|---|---|---|---|---|---|
| Fall 2025 | Wang, Patrick 3.5Rate My ProfessorsQuality3.5Difficulty2.5Would retake50%Based on 4 ratingsClick to view on RMP → | — | — | 5.2 | 96 |
| Fall 2024 | Wang, Patrick 3.5Rate My ProfessorsQuality3.5Difficulty2.5Would retake50%Based on 4 ratingsClick to view on RMP → | — | — | 4.6 | 100 |
| Fall 2023 | Wang, Patrick 3.5Rate My ProfessorsQuality3.5Difficulty2.5Would retake50%Based on 4 ratingsClick to view on RMP → | 3.7 | 2.8 | 5.0 | 80 |
Instructor
Wang, PatrickELEC&CMP
Also teaches
IDS-703 INTRO NATURAL LANGUAGE PROCESS3.8