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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 | Deep Learning, Distributed Representations | DL Ch. 6 (7-8) | Proposal/Project Information | Slides (1), Slides (2) | 3 | Representation Learning (RepL) | DL Ch. 7-8, 9, & 11 | Slides (1), Slides (2) | 4 | Recurrent Neural Networks (RNNs) | DL Ch. 10 | Slides (1), Slides (2) | 5 | Generative Models, Variational Inference | DL Ch. 14-15 (13) | Project proposals due 2/28 | Slides (1) | 6 | Generative Models | Slides (1), Slides (2) | 7 | Proposal Presentations (Day #1 & #2) | DL Ch. 16-17 | Slides (1); Paper (1), Paper (2) | 8 | Brain-Inspired Models: Harmoniums & Markov Chain Monte Carlo (RBMs/MCMC) | DL Ch. 17, 20 (18) | Slides (1); Paper (1), Paper (2) | 9 | Guest Lecture: Spiking Neural Networks | DL Ch. 19 | Slides (1); Slides (2) | 10 | Generative Adversarial Networks (GANs) | Paper (1), Paper (2) | 11 | Diffusion Models, Hugging Face (Gradio; Guest Talk) | DL Ch. 19 | Paper (1), Paper (2) | 12 | Bayesian Neural Networks (Guest Talk) | Slides (1) | 13 | Fairness & Bias in Machine Learning | Paper (1), Paper (2) | 14 | Variational Inference & Attention | Paper (1), Paper (2) | 15 (May 9, 1:30 to 4pm) | Team Final Project Presentations | Final project materials due May 9 |