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RIT Cognitive Science |
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RIT Cognitive Science |
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Disclaimer: We may get ahead of (or fall behind) this schedule, I will try to keep this up to date but regardless, homework topics will follow the actual lecture topic pace.
| Week | Topics | Homework | Reading | Special Events and Due Dates | Slides & Lecture Notes |
|---|---|---|---|---|---|
| 1 (8/26) | Introduction, high-performance research computing (HPC), Python | NP Ch. 1, 2 | Slides (1), Slides (2) | ||
| 2 (9/2+4) | Vectors, matrices, arrays | NP Ch. 3 | Slides (1), Slides (2), Perceptron Paper | ||
| 3 (9/9+11) | Manipulating tensors ("ndarrays") | NP Ch. 5 | Slides (1), Slides (2) | ||
| 4 (9/16+18) | Guest lecture: Using RIT RC (Viet) | NP Ch. 6 | Slides (1), Slides (2) | ||
| 5 (9/23+25) | Linear algebra | Final Project Assigned | NP Ch. 6, Ch. 7 | Slides (1), Slides (2) | |
| 6 (9/30, 10/2) | Numerical optimization | HW #1 (assigned) | Paper | Slides (1), Slides (2) | |
| 7 (10/7+9) | Numerical optimization | NP Ch. 9 | Slides (1) | ||
| 8 (10/16) | Optimization (No class on 10/14, Fall break) | NP Ch. 11 | Slides (1), Slides (2) | ||
| 9 (10/21+23) | Guest lecture: Spiking neural networks (William) | NP Ch. 12, Ch. 13 | |||
| 10 (10/28) | Derivatives & optimization | HW #2 (assigned) | Slides (1) | ||
| 11 (10/30) | Derivatives & optimization | Slides (1), Slides (Derivatives) | |||
| 12 (11/4+6) | Guest lecture: Predictive coding (Faeze), Bayesian modeling | NP Ch. 16 | Slides (1), Slides (2) | ||
| 13 (11/11+13) | Regression (optimization perspective) | ||||
| 14 (11/18+20) | Guest lecture: Active inference PGMs (Viet) | Slides (1), Slides (2) | |||
| 15 (11/25) | (No class on 11/27, Thanksgiving break) | Slides (1) | |||
| 16 (12/2+4) | Team paper talks, concluding remarks | Slides (1) | |||
| 16 (12/X) | Final Exam - 12/12, 1:00-3:30pm | Final Exam Project | |||