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 |
|---|---|---|---|---|---|---|---|---|---|
| 29936D | 906 | Lecture | 9:00-10:30am | MTuW | 77 of 120 | Michael Shindler | OHE122 | PDF (227954 KB) |   |
| 29937R | 906 | Discussion | 12:30-1:30pm | Tuesday | 77 of 120 | OHE136 | | ||
| 29987R | 906 | Quiz | TBA | TBA | 77 of 120 | OFFICE | | ||
| 29938D | 911 | Lecture | 9:00-10:30am | MTuW | 18 of 20 | Michael Shindler | DEN@Viterbi | PDF (227954 KB) | |
| 29939R | 911 | Discussion | 12:30-1:30pm | Tuesday | 18 of 20 | DEN@Viterbi | | ||
| 29988R | 911 | Quiz | TBA | TBA | 18 of 20 | DEN@Viterbi | |