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 |
|---|---|---|---|---|---|---|---|---|---|
| 29995R | 048 | Lecture | 2:00-4:20pm | Friday | 78 of 240 | Dani Yogatama | SGM124 |  | |
| 30151R | 048 | Discussion | 4:30-5:20pm | Friday | 78 of 240 | Dani Yogatama | SGM124 | | |
| 29984R | 048 | Quiz | TBA | TBA | 78 of 240 | OFFICE | | ||
| 30259D | 034 | Lecture | 2:00-4:20pm | Friday | 4 of 30 | Dani Yogatama | DEN@Viterbi | | |
| 30272R | 034 | Discussion | 4:30-5:20pm | Friday | 4 of 30 | Dani Yogatama | DEN@Viterbi | | |
| 29985R | 034 | Quiz | TBA | TBA | 4 of 30 | DEN@Viterbi | |