Workshop Agenda
Tuesday, August 27, 2019, Reitz Union Room 2335
| Time | Event | Speaker/facilitator | Lessons |
|---|---|---|---|
| 8:00 | Introductions and Conda setup | Adam Rivers | Lesson |
| 9:00 | Python for ML warm up | Brian Stucky | Lesson |
| 10:30 | Break / Question time | ||
| 10:45 | Framing ML Problems | Adam Rivers | Lesson |
| 11:15: | Descending into ML | Gaurav Vaidya | Lesson |
| 12:30 | Lunch | ||
| 1:45 | Job announcements | ||
| 2:00 | Reducing loss/ optimization as learning | Adam Rivers | Lesson |
| 2:30 | Generalization | Dimitri Bourilkov | Presentation |
| 3:30 | Break / Question time | ||
| 3:45 | Training and test Sets | Ravin Poudel | Lesson |
| 4:30 | Feature representation | Adam Rivers | Lesson |
| 5:00 | End of day 1 |
Wednesday, August 28, 2019 Reitz Union Room 2365
| Time | Event | Speaker/facilitator | Lessons |
|---|---|---|---|
| 8:00 | Regularization | Brian Stucky | Lesson |
| 9:40 | Break / Question time | ||
| 9:50 | Logistic regression | Geraldine Klarenberg | Lesson |
| 10:45 | Classification metrics | Geraldine Klarenberg | Lesson |
| 11:30 | Synthesis example | Adam Rivers | Lesson |
| 12:10 | Group Picture | ||
| 12:25 | Lunch | ||
| 1:30 | Tree based methods | Dimitri Bourilkov | Lesson |
| 2:15 | Neural network methods | Gaurav Vaidya | Lesson |
| 3:15 | Break / Question time | ||
| 3:30 | The landscape of ML methods | Adam Rivers | Lesson |
| 4:00 | The landscape of software tools available | Adam Rivers | Lesson |
| 4:15 | Resources for learning more about ML once you go home | Adam Rivers | Lesson |
| 4:25 | Instructors answering questions / Time to talk with colleagues | ||
| 5:00 | End of Workshop |