EE 142: 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 CS 171.
Average GPA: 2.92
Grade distribution records: 256 students across 11 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 35 | 13.7% |
| A | 25 | 9.8% |
| A- | 28 | 10.9% |
| B+ | 24 | 9.4% |
| B | 37 | 14.5% |
| B- | 23 | 9.0% |
| C+ | 24 | 9.4% |
| C | 20 | 7.8% |
| C- | 16 | 6.3% |
| D+ | 9 | 3.5% |
| D- | 1 | 0.4% |
| F | 8 | 3.1% |
| S | 1 | 0.4% |
| W | 5 | 2.0% |
Based on 256 student grade records across 11 terms and 2 professors.
Instructors
- Salman Asif 94 students, Average GPA 3.03
- Evangelos Papalexakis 16 students, Average GPA 3.09