Computer Science 567:
Machine Learning (4.0 units)
Statistical methods for building intelligent and adaptive systems that improve performance from experiences; Focus on theoretical understanding of these methods and their computational implications. Recommended preparation: Undergraduate level training or coursework in linear algebra, multivariate calculus, basic probability and statistics; an undergraduate level course in Artificial Intelligence may be helpful but is not required.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 29930D | 902 | Lecture | 1:00-3:50pm | TuWTh | 45 of 70 | Victor Adamchik | OHE132 |   | |
| 29931R | 902 | Discussion | TBA | TBA | 45 of 70 | OFFICE | | ||
| 29962R | 902 | Quiz | TBA | TBA | 45 of 70 | OFFICE | | ||
| 29932D | 910 | Lecture | 1:00-3:50pm | TuWTh | 6 of 15 | Victor Adamchik | DEN@Viterbi | | |
| 29933R | 910 | Discussion | TBA | TBA | 6 of 15 | OFFICE | | ||
| 29963R | 910 | Quiz | TBA | TBA | 6 of 15 | OFFICE | |