CS 170: INTRODUCTION TO ARTIFICIAL INTELLIGENCE
University of California, Riverside
4 Units, Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): CS 100 with a grade of "C-" or better, CS 111. An introduction to the field of artificial intelligence. Focuses on discrete-valued problems. Covers heuristic search, problem representation, and classical planning. Also covers constraint satisfaction and logical inference.
Average GPA: 3.41
Grade distribution records: 2,987 students across 28 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 427 | 14.3% |
| A | 850 | 28.5% |
| A- | 458 | 15.3% |
| B+ | 332 | 11.1% |
| B | 299 | 10.0% |
| B- | 215 | 7.2% |
| C+ | 122 | 4.1% |
| C | 86 | 2.9% |
| C- | 52 | 1.7% |
| D+ | 17 | 0.6% |
| D | 13 | 0.4% |
| D- | 18 | 0.6% |
| F | 37 | 1.2% |
| NP | 2 | 0.1% |
| S | 14 | 0.5% |
| W | 45 | 1.5% |
Based on 2,987 student grade records across 28 terms and 6 professors.
Instructors
- Sofia Sakellaridi 498 students, Average GPA 3.24
- Eamonn Keogh 473 students, Average GPA 3.61
- Niloofar Montazeri 417 students, Average GPA 3.33
- Nader Shakibay Senobari 216 students, Average GPA 3.18
- Neftali Watkinson Medina 159 students, Average GPA 3.51
- Paea Lependu 28 students, Average GPA 3.93