The primary textbook for this course is freely available online and is as follows:
I also strongly recommend the following textbook, which can be downloaded for free when connected to the CU network or VPN. It supplements the previous one with more recent neural network approaches and programming tutorials:
| Date | Topics | Assigned Readings for Class | Assignments Due Before Class (posted on Canvas) |
|---|---|---|---|
| Mon, Jan 10 | Course introduction (lecture slides) |
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| Wed, Jan 12 | Artificial neurons (lecture slides) |
Ch. 1 of Goodfellow book, Ch. 1-2.5 and 4-4.2 of Kamath book | |
| Mon, Jan 17 | No class (Martin Luther King, Jr. holiday) | ||
| Wed, Jan 19 | Feedforward neural networks (lecture slides) |
Ch. 6-6.4 of Goodfellow book | Problem set 1 |
| Mon, Jan 24 | Gradient descent and training neural networks (lecture slides) |
Ch. 4-5.1, 5.9-5.10, and 6.5-6.6 of Goodfellow book, Ch. 4.3-4.42 of Kamath book | |
| Wed, Jan 26 | Neural network training (lecture slides) |
Ch. 5.2-5.6, 5.11, 8-8.6 of Goodfellow book, Ch. 4.4.3 of Kamath book | |
| Mon, Jan 31 | Convolutional neural networks (lecture slides) |
Ch. 9-9.5 and 9.10-9.11 of Goodfellow book, (Optional) Ch. 6-6.3 of Kamath book | Lab assignment 1 |
| Wed, Feb 2 | No class (cancelled because of winter storms) | ||
| Mon, Feb 7 | Introduction to computer vision, image classification (lecture slides) |
Ch. 12.2 of Goodfellow book, Ch. 6.5-6.6 of Kamath book | Problem set 2 |
| Wed, Feb 9 | Regularization (lecture slides) |
Ch. 7 of Goodfellow book, Ch. 4.5 of Kamath book | |
| Mon, Feb 14 | Pretrained CNN features and fine-tuning (lecture slides) |
Ch. 12-12.1 of Goodfellow book | |
| Wed, Feb 16 | Object detection and semantic segmentation (lecture slides) |
Ch. 9.6-9.7 of Goodfellow book, Faster R-CNN, Fully Convolutional Networks for Semantic Segmentation | Lab assignment 2 |
| Mon, Feb 21 | Recurrent neural networks (lecture slides) |
Ch. 10 of Goodfellow book, (Optional) Ch. 7.1-7.4 of Kamath book | |
| Wed, Feb 23 | Introduction to natural language processing, neural word embeddings (lecture slides) |
Ch. 12.4 of Goodfellow book, Ch. 5.1-5.5 of Kamath book | Problem set 3 |
| Mon, Feb 28 | Introduction to attention (lecture slides) |
Ch. 7.5 and 9-9.2.5 of Kamath book | |
| Wed, Mar 2 | Transformers (lecture slides) |
Ch. 9.2.6-9.2.10 of Kamath book | |
| Mon, Mar 7 | Popular transformers (lecture slides) |
Improving Language Understanding by Generative Pre-Training | Lab assignment 3 |
| Wed, Mar 9 | Multimodal neural networks: image captioning (lecture slides) |
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention | |
| Mon, Mar 14 | Multimodal neural networks: visual question answering (lecture slides) |
VQA: Visual Question Answering | Problem set 4 |
| Wed, Mar 16 | Multimodal neural networks: visual dialog (lecture slides) |
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| Mon, Mar 21 | No class (spring break) | ||
| Wed, Mar 23 | No class (spring break) | ||
| Mon, Mar 28 | No class (cancelled) | ||
| Wed, Mar 30 | Transfer learning: self-supervised learning (lecture slides) |
Ch. 4.6 and 10-10.3 of Kamath book | Lab assignment 4 (extension provided; due on Friday) |
| Mon, Apr 4 | Transfer learning: multi-task learning and few/zero shot learning (lecture slides) |
Ch. 10.4-11.2 of Kamath book | |
| Wed, Apr 6 | Model compression (lecture slides) |
Final project proposal (extension provided; due on Friday) | |
| Mon, Apr 11 | Efficient learning and deep reinforcement learning (lecture slides) |
Ch. 13 of Kamath book | |
| Wed, Apr 13 | Deep learning for speech processing (lecture slides) |
Ch. 8 and 12 of Kamath book | Final project outline (extension provided; due on Friday) |
| Mon, Apr 18 | Deep learning in industry (Guest speaker: Suyog Jain, PathAI) |
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| Wed, Apr 20 | Deep learning in industry (Guest speaker: Sanaz Bahargam, Amazon) |
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| Mon, Apr 25 | Ethical deep learning (Guest speaker: Mehrnoosh Sameki, Microsoft) |
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| Wed, Apr 27 | Course finale | Final project video (due today), peer evaluation (due tomorrow) | |
| Fri, May 6 | No class (final exam week) | Final project report |