Computer Science 559:
Machine Learning I: Supervised Methods (4.0 units)
Distribution-free and probabilistic methods for supervised classification and regression; learning algorithms; optimization techniques; feature-space transformations; parametric and nonparametric methods; Bayes decision theory; artificial neural networks.
- Corequisite: EE 503 and EE 510
- Crosslist: This course is offered by the EE department but may qualify for major credit in CSCI. To register, enroll in EE 559.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 30490R | 048 | Lecture | 3:30-5:20pm | Mon, Wed | 42 of 60 | Keith Chugg | DMC156 |  | |
| 30727R | 048 | Discussion | 5:30-6:20pm | Thursday | 43 of 60 | Keith Chugg | GFS116 | | |
| 30561D | 034 | Lecture | 3:30-5:20pm | Mon, Wed | 3 of 60 | Keith Jenkins | DEN@Viterbi | | |
| 30489R | 034 | Discussion | 5:30-6:20pm | Thursday | 3 of 60 | Keith Jenkins | DEN@Viterbi | | |
| 30728R | 048 | Lecture | 3:30-5:20pm | Mon, Wed | 60 of 60 | Keith Jenkins | OHE122 | | |
| 30565R | 048 | Discussion | 5:30-6:20pm | Thursday | 59 of 60 | Keith Jenkins | OHE122 | |