CMPSC 165B: MACHINE LEARNING
University of California, Santa Barbara
Covers the most important techniques of machine learning (ML) and includes discussions of: well-posed learning problems; artificial neural networks; c oncept learning and general to specific ordering; decision tree learning; g enetic algorithms; Bayesian learning; analytical learning; and others.
Average GPA: 3.43
Grade distribution records: 1,680 students across 25 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 236 | 14.0% |
| A | 509 | 30.3% |
| A- | 292 | 17.4% |
| B+ | 208 | 12.4% |
| B | 186 | 11.1% |
| B- | 79 | 4.7% |
| C+ | 40 | 2.4% |
| C | 49 | 2.9% |
| C- | 13 | 0.8% |
| D+ | 2 | 0.1% |
| D | 27 | 1.6% |
| D- | 1 | 0.1% |
| F | 38 | 2.3% |
Based on 1,680 student grade records across 25 terms and 12 professors.
Instructors
- Wang Y W 500 students, Average GPA 3.41
- Yan X 262 students, Average GPA 3.54
- Turk M A 203 students, Average GPA 3.24
- Guo W 144 students, Average GPA 3.69
- Wang Y F 131 students, Average GPA 3.05
- Chang Shiyu 105 students, Average GPA 3.84
- Wang E 100 students, Average GPA 3.55
- Ding Yufei 74 students, Average GPA 2.99
- Li Lei 54 students, Average GPA 3.19
- Wang Yuxiang 46 students, Average GPA 3.80
- Kozerawski J 39 students, Average GPA 3.76
- Singh A K 22 students, Average GPA 2.92