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RIT Department of Computer Science |
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RIT Department of Computer Science |
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Disclaimer: As we may get ahead of (or fall behind) this schedule, I will
try to keep this up to date. Regardless, quiz/homework topics
will follow the actual lecture topic pace.
Note that book chapters in parentheses (*) are optional but highly recommended auxiliary/supporting reading.
| Week | Topics | Homework | Reading | Special Events and Due Dates | Slides & Lecture Notes |
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| 1 | Introduction, Review: Statistical Learning | Slides (1), Slides (2) | 2 | ML Review, Deep Learning | DL Ch. 6 (7-8) | Proposal/Project Information | Slides (1), Slides (2) | 3 | Deep Learning | DL Ch. 6-8 | Slides (1), Slides (2) | 4 | Representation Learning (RepL) | DL Ch. 15 | Slides (1) | 5 | Recurrent Neural Networks (RNNs) | DL Ch. 10 | Project proposals due 2/28 | Slides (1) | 6 | Generative Models & Uncertainty | Slides (1), Slides (2) | 7 | Generative Models & Uncertainty | 8 | Proposal Presentations / Talks | 9 | Guest Lecture: Spiking Neural Networks | DL Ch. 19 | Slides (1); Slides (2) | 10 | Diffusion Models | 11 | Diffusion Models & Reinforcement Learning | Slides (1) | 12 | Reinforcement Learning | Slides (1) | 13 | Reinforcement Learning | 14 | Out-of-distribution Detection, Conclusion | Slides (1) | 15 (May 6, 1:30 to 4pm) | Team Final Project Presentations | Final report due May 6, 11:59pm |