Introduction to machine learning algorithms. Covers learning models from fields of statistical decision theory and pattern recognition, artificial intelligence, and theoretical computer science. Topics include classification learning and the Probably Approximately Correct (PAC) learning framework, density estimation and Bayesian learning, EM, regression, and online learning. Provides an introduction to standard learning methods such as neural networks, decision trees, boosting, nearest neighbor, and support vector machines. Requirements include one major experimental learning project or theoretical paper. Students may not receive credit for both this course and CSE 142. (Formerly CMPS 242.)
7 terms
Other grades
University of California, Santa Cruz
CSE 242 at UCSC: average GPA 3.66 across 440 students. Grade distribution by term. Introduction to machine learning algorithms. Covers learning models from…
Average GPA: 3.66
Grade distribution records: 440 students across 7 terms.
| Grade | Students | Percent |
|---|---|---|
| A+ | 81 | 18.4% |
| A | 133 | 30.2% |
| A- | 74 | 16.8% |
| B+ | 32 | 7.3% |
| B | 23 | 5.2% |
| B- | 5 | 1.1% |
| C | 10 | 2.3% |
| D | 7 | 1.6% |
| F | 2 | 0.5% |
| P | 2 | 0.5% |
| S | 64 | 14.5% |
| U | 3 | 0.7% |
| W | 1 | 0.2% |
Based on 440 student grade records across 7 terms and 4 professors.
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