Note: This syllabus is subject to change based on the needs of the class
| Event Type | Date | Description | Course Material |
| Lecture | Aug 28 | Overview of Deep Learning Applications |
[slides]
|
| Lecture | Aug 28, Sep 4 |
History of Deep Learning Assignment #0 available |
[slides] |
| Lecture | Sep 11 | Stochastic Gradient Descent, Neural Networks, & Backpropagation | [slides] |
| Lecture | Sep 18 |
Neural Networks & Backpropagation (cont.) Asssigment #0 due
|
[slides] |
| Lecture | Sep 25 |
Neural Networks & Backpropagation (cont.) Introduction to ConvNets (architectures – AlexNet, VGG, Inception, ResNet, DenseNet – loss surface,…) Guest Lecture: Erik Enquist, Rice RCSG Assignment #1 available |
[slides] [slides] |
| Lecture | Oct 2 |
Introduction to ConvNets (architectures – AlexNet, VGG, Inception, ResNet, DenseNet – loss surface,…) (cont.) Training ConvNets |
[slides] [slides] |
| Lecture | Oct 9 |
Species of ConvNets Assignment #1 due |
[slides] |
| Lecture | Oct 16 |
Understanding and Visualizing Convnets & Introduction to Recurrent Neural Networks
|
[slides] |
| Lecture | Oct 23 |
Recurrent Neural Networks Applications Assignment #2 available |
[slides] |
| Lecture | Oct 30 | Recurrent Neural Networks Applications (cont.) | [slides] |
| Lecture | Nov 6 |
Recurrent Neural Networks Applications (cont.) Assignment #2 due |
[slides] |
| Lecture | Nov 13 |
Deep Reinforcement Learning Understanding Neural Nets as Splines |
[slides] [slides] |
| Lecture | Nov 20 |
Understanding Neural Nets as Splines (cont.) Deep Reinforcement Learning Final Project Proposal due |
[slides] [slides] |
| Lecture | Nov 27 | Thanksgiving – No class | |
| Lecture | Dec 4 |
Guest Lecture: Alan Lockett (NLP Expert) NLP + Deep Learning, Large Language Models |
|
| Final Project | Dec 11 & 12 Dec 17, 23:59 |
Project Presentation
Project Report Due |