HOML = Hands-On Machine Learning
DL = Deep Learning (these are generally optional readings, but fill in a lot of great detail)
| Class # | Date | Topic | Reading | Assignment | Due |
| 1 | Tue, Jan 14 | Class Introduction | Syllabus | ||
| 2 | Thu, Jan 16 | Multi-Layer Neural Networks Background: Python Basics | HOML CH 10 (pp. 279-295) Depth: DL CH 6-6.4 Background: Introduction to Python for Programmers (video on Canvas) | ||
| 3 | Tue, Jan 21 | Backpropagation | Depth: DL CH 6.5 | ||
| 4 | Thu, Jan 23 | Jupyter and our Computational Environment | HOML CH 10 (pp. 295-314) | ||
| 5 | Tue, Jan 28 | Visualizing and Tuning Models | HOML CH 10 (rest) | ||
| 6 | Thu, Jan 30 | Training Deep Networks I | HOML CH 11 (pp. 331-351) Depth: DL CH 8-8.3) | HW 1 | |
| 7 | Tue, Feb 4 | Training Deep Networks II | HOML CH 11 (rest) Depth: DL CH 8.4-8.6 | ||
| 8 | Thu, Feb 6 | Data Handling in TensorFlow I | HOML CH 13 (pp. 413-430) | HW 2 | HW 1 |
| 9 | Tue, Feb 11 | Data Handling in TensorFlow II | HOML CH 13 (rest) | ||
| 10 | Thu, Feb 13 | Convolutional Neural Networks I | HOML CH 14 (pp. 445-453) Depth: DL 9-9.2, 9.10 | HW 3 | HW 2 |
| 11 | Tue, Feb 18 | Convolutional Neural Networks II | HOML CH 14 (pp. 453-483) Depth: DL 9.3-9.4 | ||
| 12 | Thu, Feb 20 | Convolutional Neural Networks III | HOML CH 14 (rest) | HW 4 | HW 3 |
| 13 | Tue, Feb 25 | Reinforcement Learning I | HOML CH 18 (pp. 609-630) | ||
| 14 | Thu, Feb 27 | Reinforcement Learning II | HOML CH 18 (pp. 630-650) | HW 5 | HW 4 |
| 15 | Tue, Mar 3 | Project discussion | n/a | Presentation | |
| 16 | Thu, Mar 5 | Reinforcement Learning III | HOML CH 18 (rest) | ||
| 17 | Tue, Mar 10 | Reinforcement Learning IV | TBA | ||
| 18 | Thu, Mar 12 | Autoencoders | HOML CH 17 (pp. 567-579) Depth: DL 14-14.5 | HW 6 | HW 5 |
| - | Tue, Mar 17 | Holiday | |||
| - | Thu, Mar 19 | Holiday | |||
| 19 | Tue, Mar 24 | Convolutional-Variational Autoencoders | HOML CH 17 (pp. 579-591) Depth: DL 14.6-14.7 | ||
| 20 | Thu, Mar 26 | Project Checkpoint | n/a | Presentation | |
| 21 | Tue, Mar 31 | Generative Adversarial Networks | HOML CH 17 (rest) | ||
| 22 | Thu, Apr 2 | Recurrent Neural Networks | HOML CH 15 (pp. 497-511) Depth: DL 10-10.2 | HW 6 | |
| 23 | Tue, Apr 7 | Recurrent Neural Networks: Memory | HOML CH 15 (pp. 511-523) Depth: DL 10.7- | ||
| 24 | Thu, Apr 9 | Recurrent Neural Networks: Natural Language Proccessing | HOML CH 16 (pp. 525-542) Depth: DL 12.4-12.4.4 | ||
| 25 | Tue, Apr 14 | Project Checkpoint | n/a | Presentation | |
| 26 | Thu, Apr 16 | Recurrent Neural Networks: Machine Translation and Attention | HOML CH 16 (pp. 542-554) Depth: DL 12.4.5 | ||
| 27 | Tue, Apr 21 | Recurrent Neural Networks: Attention | HOML CH 16 (pp. 554-565) | ||
| 28 | Thu, Apr 23 | Tensorflow Internals | HOML CH 12 (pp. 375-394) | ||
| 29 | Tue, Apr 28 | Tensorflow Internals | HOML CH 12 (rest) | ||
| 30 | Thu, Apr 30 | Final Project Presentations | n/a | Final Presentations (a few) | |
| Fri, May 1 | Appendix submission | ||||
| 31 | Tue, May 5 | Final presentations 4:30-6:30 | n/a | ||
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