CS 171: INTRODUCTION TO MACHINE LEARNING AND DATA MINING
University of California, Riverside
4 Units, Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): MATH 010A; MATH 031 or EE 020B; STAT 155 or EE 114 or STAT 156A or STAT 160A; CS 100 or EE 016. Introduces formalisms and methods in data mining and machine learning. Topics include data representation, supervised learning, and classification. Covers regression and clustering. Also covers rule learning, function approximation, and margin-based methods. Cross-listed with EE 142.
Average GPA: 3.07
Grade distribution records: 1,503 students across 27 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 201 | 13.4% |
| A | 257 | 17.1% |
| A- | 193 | 12.8% |
| B+ | 146 | 9.7% |
| B | 162 | 10.8% |
| B- | 107 | 7.1% |
| C+ | 93 | 6.2% |
| C | 109 | 7.3% |
| C- | 62 | 4.1% |
| D+ | 27 | 1.8% |
| D | 27 | 1.8% |
| D- | 15 | 1.0% |
| F | 37 | 2.5% |
| NP | 5 | 0.3% |
| S | 2 | 0.1% |
| W | 60 | 4.0% |
Based on 1,503 student grade records across 27 terms and 3 professors.
Instructors
- Evangelos Papalexakis 408 students, Average GPA 3.23
- Christian Shelton 276 students, Average GPA 2.71
- Salman Asif 230 students, Average GPA 3.09