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 |
|---|---|---|---|---|---|---|---|---|---|
| 29907R | 902 | Lecture | 11:00-1:50pm | MWTh | 58 of 80 | Victor Adamchik |   | ||
| 29908R | 902 | Discussion | TBA | TBA | 58 of 100 | OFFICE | | ||
| 29909R | 902 | Quiz | TBA | TBA | 58 of 100 | OFFICE | | ||
| 29937D | 910 | Lecture | 11:00-1:50pm | MWTh | 1 of 30 | Victor Adamchik | DEN@Viterbi | | |
| 29938R | 910 | Discussion | TBA | TBA | 1 of 30 | DEN@Viterbi | | ||
| 29939R | 910 | Quiz | TBA | TBA | 1 of 30 | DEN@Viterbi | |