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 |
|---|---|---|---|---|---|---|---|---|---|
| 29984R | 048 | Quiz | TBA | TBA | 406 of 600 | OFFICE |  | ||
| 30352D | 048 | Lecture | 5:00-7:20pm | Tuesday | 198 of 200 | Victor Adamchik | SGM123 | PDF (254658 KB) |  |
| 30392R | 048 | Discussion | 7:30-8:20pm | Tuesday | 199 of 300 | SGM123 | | ||
| 29995D | 048 | Lecture | 5:00-7:20pm | Thursday | 208 of 200 | Victor Adamchik | SGM123 | PDF (254658 KB) | |
| 30151R | 048 | Discussion | 7:30-8:20pm | Thursday | 207 of 300 | SGM123 | | ||
| 30259D | 034 | Lecture | 5:00-7:20pm | Thursday | 10 of 20 | Victor Adamchik | DEN@Viterbi | PDF (254658 KB) | |
| 30272R | 034 | Discussion | 7:30-8:20pm | Thursday | 10 of 20 | DEN@Viterbi | | ||
| 29985R | 034 | Quiz | TBA | TBA | 10 of 30 | DEN@Viterbi | |