PSTAT 131: STAT MACHINE LEARN
University of California, Santa Barbara
Statistical Machine Learning is used to discover patterns and relationships in large data sets. Topics will include: data exploration, classification and regression tress, random forests, clustering and association rules. Bui lding predictive models focusing on model selection, model comparison and p erformance evaluation. Emphasis will be on concepts, methods and data analy sis; and students are expected to complete a significant class project, ind ividual or team based, using real-world data.
Average GPA: 3.39
Grade distribution records: 3,542 students across 36 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 190 | 5.4% |
| A | 1,376 | 38.8% |
| A- | 531 | 15.0% |
| B+ | 392 | 11.1% |
| B | 418 | 11.8% |
| B- | 208 | 5.9% |
| C+ | 116 | 3.3% |
| C | 128 | 3.6% |
| C- | 42 | 1.2% |
| D+ | 20 | 0.6% |
| D | 27 | 0.8% |
| D- | 8 | 0.2% |
| F | 86 | 2.4% |
Based on 3,542 student grade records across 36 terms and 13 professors.
Instructors
- Coburn K M 1,111 students, Average GPA 3.64
- Yu G 618 students, Average GPA 3.31
- Oh Sang-Yun 415 students, Average GPA 2.84
- Li Zhijian 260 students, Average GPA 3.38
- Franks A 214 students, Average GPA 3.18
- Coburn T 213 students, Average GPA 3.62
- Ruiz T D 175 students, Average GPA 3.36
- Kloke J 146 students, Average GPA 3.57
- Feldman R 124 students, Average GPA 3.31
- Taufer E 115 students, Average GPA 2.90
- Baracaldo Lan 64 students, Average GPA 3.50
- Gopalan G 59 students, Average GPA 3.60
- Iyer S K 28 students, Average GPA 3.85