Schedule and Syllabus

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 F19]


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]

[slides F19]

Lecture Oct 16

Recurrent Neural Networks Applications

Assignment #2 available

[[slides F19]

[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