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 

Lecture  Aug 28, Sep 4 
History of Deep Learning Assignment #0 available 
[slides F19] 
Lecture  Sep 4  Stochastic Gradient Descent, Neural Networks, & Backpropagation  [slides F19] 
Lecture  Sep 11 
Neural Networks & Backpropagation (cont.) Asssigment #0 due Assignment #1 available 
[slides F19] 
Lecture  Sep 18  Introduction to ConvNets (architectures – AlexNet, VGG, Inception, ResNet, DenseNet – loss surface,…)  [slides F19] 
Lecture  Sep 25  Guest Lectures: Zhengwei Wu and Brian Anderson  
Lecture  Oct 2  Species of ConvNets  [slides F19] [slides F19] 
Lecture  Oct 9 
Understanding and Visualizing Convnets & Introduction to Recurrent Neural Networks Assignment #1 due 
[slides F19] 
Lecture  Oct 16 
Recurrent Neural Networks Applications Assignment #2 available 
[[slides F19] 
Lecture  Oct 23  No class  
Lecture  Oct 30 
Recurrent Neural Networks Applications (cont.) [slides F19] 
[slides F19] 
Lecture  Nov 6 
Recurrent Neural Networks Applications (cont.) Assignment #2 due 
[slides F19] 
Lecture  Nov 13 
Deep Reinforcement Learning Final Project Proposal due 
[slides F19] 
Lecture  Nov 20  Guest Lecture: TBD  
Lecture  Nov 27 
Thanksgiving – No class 

Final Project 
Dec 2/5 Dec 11, 23:59 
Project Presentation
Project Report Due 