Schedule
| Date | Lecture | Homework / Readings | Logistics | |
|---|---|---|---|---|
| Module 1: Deep Learning Basics | ||||
| 1/13 |
Lecture 1
(Jesse Thomason):
Course Introduction [ slides | video ] |
|
||
| 1/20 |
Lecture 2
:
Framing Problems for ML + Loss functions and Optimization [ slides | video ] |
|
||
| 1/27 | No Class | |||
| 2/3 |
Lecture 3
(Jesse Thomason):
Neural Network Overview + Convolutional Neural Networks [ slides | video ] |
Project Team Due |
||
| 2/10 |
Lecture 4
(Jesse Thomason):
Activations, Initializations, Optimization, and Regularization + Software Tutorials (by TAs) [ slides | video ] |
|||
| 2/17 |
Lecture 5
(Jesse Thomason):
Recurrent Neural Networks [ slides | video ] |
Course Project Proposal Due |
||
| Module 2: Deep Learning Applications | ||||
| 2/24 |
Lecture 6
:
Midterm Exam [ slides | video ] |
|||
| 3/3 |
Lecture 7
(Jesse Thomason):
Deep Learning for Natural Language Processing + project pitches [ slides | video ] |
|||
| 3/10 |
Lecture 8
(Jesse Thomason):
Transformer Networks and Deep Learning for Computer Vision + midterm debrief [ slides | video ] |
|
Project Survey Due |
|
| 3/17 | No Class (Spring Break) | |||
| 3/24 |
Lecture 9
(Jesse Thomason):
Multimodal Deep Learning [ slides | video ] |
|||
| Module 3: Advanced Topics in Deep Learning | ||||
| 3/31 |
Lecture 10
(Jesse Thomason):
Deep Learning for Agents + paper breakouts [ slides | video ] |
Course Project Mid-Report Due, April 3Assignment 2 Due, April 3 |
||
| 4/7 |
Lecture 11
(Jesse Thomason):
Deep Reinforcement Learning + paper breakouts [ slides | video ] |
|||
| 4/14 |
Lecture 12
(Jesse Thomason):
Deep Learning and the World + paper breakouts [ slides | video ] |
|||
| 4/21 |
Lecture 13
(Jesse Thomason):
Team Project Presentations [ slides | video ] |
|||
| 4/28 |
Lecture 14
:
Team Project Presentations [ slides | video ] |
|||
| No in-class exam, Final Report due Wed May 3 11:59pm | ||||