Schedule
| Date | Lecture | Readings | Logistics | |
|---|---|---|---|---|
| Module 1: Introduction and Foundations | ||||
| 9/3 |
Lecture #1
(Prof. Lengerich):
Course Introduction, Introduction to DL [ slides | notes ] |
|||
| 9/8 |
Lecture #2
(Prof. Lengerich):
A Brief History of DL [ slides | notes ] |
HW1 Out |
||
| 9/10 |
Lecture #3
(Prof. Lengerich):
Statistics / linear algebra / calculus review [ slides | notes ] |
|||
| 9/15 |
Lecture #4
(Prof. Lengerich):
Single-layer networks [ slides | notes ] |
|||
| 9/17 |
Lecture #5
(Prof. Lengerich):
Parameter Optimization and Gradient Descent [ slides | notes ] |
HW2 Out |
||
| 9/22 |
Lecture #6
(Prof. Lengerich):
Automatic differentiation with PyTorch [ slides | notes ] |
|||
| 9/24 |
Lecture #7
(Prof. Lengerich):
Cluster and cloud computing resources [ slides | notes ] |
|||
| Module 2: Neural Networks | ||||
| 9/29 |
Lecture #8
(Prof. Lengerich):
Multinomial logistic regression [ slides | notes ] |
|||
| 10/1 |
Lecture #9
(Prof. Lengerich):
Multi-layer perceptrons and backpropagation [ slides | notes ] |
HW3 Out |
||
| 10/6 |
Lecture #10
(Prof. Lengerich):
Regularization [ slides | notes ] |
|||
| 10/8 |
Lecture #11
(Prof. Lengerich):
Normalization / Initialization [ slides | notes ] |
|||
| 10/13 |
Lecture #12
(Prof. Lengerich):
Optimization, Learning Rates [ slides | notes ] |
|||
| 10/15 |
Lecture #13
(Prof. Lengerich):
CNNs [ slides | notes ] |
Project Proposal Due |
||
| 10/20 |
Lecture #14
(Prof. Lengerich):
Review [ slides | notes ] |
|||
| 10/22 | Midterm Exam | |||
| Module 3: Intro to Generative Models | ||||
| 10/27 |
Lecture #15
(Prof. Lengerich):
A Linear Intro to Generative Models [ slides | notes ] |
|||
| 10/29 |
Lecture #16
(Prof. Lengerich):
Factor Analysis, Autoencoders, VAEs [ slides | notes ] |
|||
| 11/3 |
Lecture #17
(Prof. Lengerich):
Generative Adversarial Networks [ slides | notes ] |
|||
| 11/5 |
Lecture #18
(Prof. Lengerich):
Diffusion Models [ slides | notes ] |
Project Midway Report Due |
||
| Module 4: Large Language Models | ||||
| 11/10 |
Lecture #19
(Prof. Lengerich):
Sequence Learning with RNNs [ slides | notes ] |
HW4 Out |
||
| 11/12 |
Lecture #20
(Prof. Lengerich):
Attention, Transformers [ slides | notes ] |
|||
| 11/17 |
Lecture #21
(Prof. Lengerich):
GPT Architectures [ slides | notes ] |
|||
| 11/19 |
Lecture #22
(Prof. Lengerich):
Unsupervised Training of LLMs [ slides | notes ] |
|||
| 11/24 |
Lecture #23
(Prof. Lengerich):
Supervised Fine-tuning of LLMs [ slides | notes ] |
HW5 Out |
||
| 11/26 |
Lecture #24
(Prof. Lengerich):
Prompts and In-context learning [ slides | notes ] |
|||
| 12/1 |
Lecture #25
(Prof. Lengerich):
Foundation models, alignment, explainability [ slides | notes ] |
|||
| 12/3 |
Lecture #26
(Prof. Lengerich):
Open directions in LLM research [ slides | notes ] |
|||
| Module 5: Student Presentations | ||||
| 12/8 | Project Presentations | |||
| 12/10 | Project Presentations | |||
| 12/17 | Final Exam | |||