COMP 442: MACHINE LEARNING
California State University, Northridge
Prerequisites: Grades of "C-" or better in COMP 380/L and MATH 340. For Data Science Minors, Prerequisite: COMP 182/L, Pre/Corequisite: MATH 444. A study of the concepts, principles, techniques, and applications of machine learning. Topics include concept-based learning, information-based learning (decision trees and ID3 algorithms), rule-based learning (association rules, learning ordered rules, learning unordered rules, and descriptive rule learning), distance-based learning (nearest neighbor algorithms), probability-based learning (Bayesian classifiers and networks), and error-based learning (perceptron, multivariable linear regression with gradient descent, nonlinear and multidimensional models, artificial neural networks, and support vector machines). Model ensembles learning and reinforcement learning are also discussed. Available for graduate credit. Graduate students will be required to complete advanced projects.
Average GPA: 2.85
Grade distribution records: 154 students across 5 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A | 37 | 24.0% |
| A- | 14 | 9.1% |
| B+ | 10 | 6.5% |
| B | 30 | 19.5% |
| B- | 12 | 7.8% |
| C+ | 12 | 7.8% |
| C | 18 | 11.7% |
| C- | 4 | 2.6% |
| D+ | 1 | 0.6% |
| D | 11 | 7.1% |
| D- | 1 | 0.6% |
| F | 4 | 2.6% |
Based on 154 student grade records across 5 terms and 2 professors.
Instructors
- Mansoureh Lord 69 students, Average GPA 2.63
- Joel Cruz Rarang 31 students, Average GPA 3.37