CS 171: Introduction to Machine Learning
San Jose State University
Covers a selection of classic machine learning techniques including backpropagation and several currently popular neural networking and deep learning architectures. Hands-on lab exercises are a significant part of the course. A major project is required. Prerequisite(s): CS 146 (with a grade of C- or better). Computer Science, Data Science, Computer Science and Linguistics, or Software Engineering majors only.
Average GPA: 3.36
Grade distribution records: 797 students across 10 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 38 | 4.8% |
| A | 314 | 39.4% |
| A- | 69 | 8.7% |
| B+ | 80 | 10.0% |
| B | 168 | 21.1% |
| B- | 31 | 3.9% |
| C+ | 25 | 3.1% |
| C | 44 | 5.5% |
| C- | 8 | 1.0% |
| D+ | 4 | 0.5% |
| F | 16 | 2.0% |
Based on 797 student grade records across 10 terms and 7 professors.
Instructors
- Fabio di Troia 327 students, Average GPA 3.41
- Tahereh Arabghalizi 148 students, Average GPA 3.43
- Saptarshi Sengupta 93 students, Average GPA 3.16
- Mira Jane 86 students, Average GPA 3.44
- Michael Hamilton Wood 59 students, Average GPA 3.63
- Samuel Chen 43 students, Average GPA 2.90
- Nagib Zahi Hakim 41 students, Average GPA 3.13