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 |
|---|---|---|---|---|---|---|---|---|---|
| 29922R | 901 | Lecture | 1:30-4:20pm | TuWTh | 37 of 40 | Victor Adamchik | RTH109 |  | |
| 29923R | 901 | Discussion | TBA | TBA | 37 of 40 | OFFICE | | ||
| 29924R | 901 | Quiz | TBA | TBA | 37 of 40 | OFFICE | | ||
| 29925D | 909 | Lecture | 1:30-4:20pm | TuWTh | 13 of 30 | Victor Adamchik | DEN@Viterbi | | |
| 29926R | 909 | Discussion | TBA | TBA | 13 of 30 | DEN@Viterbi | | ||
| 29927R | 909 | Quiz | TBA | TBA | 13 of 30 | DEN@Viterbi | |