Schedule
| Date | Lecture | Logistics | ||
|---|---|---|---|---|
| 9/8 |
Lecture 1:
Introduction
[ slides ] |
|||
| 9/13 |
Lecture 2:
Basics of Deep Learning
[ slides | video ] |
Scribe duties assignment out Lab 0 Tutorial |
||
| Efficient Inference | ||||
| 9/15 |
Lecture 3:
Pruning and Sparsity (Part I)
[ slides | video | demo ] |
Lab 1 out Lab 0 due (not counted to final grade) |
||
| 9/20 |
Lecture 4:
Pruning and Sparsity (Part II)
[ slides | video | video (zoom) ] |
|||
| 9/22 |
Lecture 5:
Quantization (Part I)
[ slides | video | video (zoom) ] |
|||
| 9/27 |
Lecture 6:
Quantization (Part II)
[ slides | video | video (zoom) ] |
Lab 2 out | ||
| 9/29 |
Lecture 7:
Neural Architecture Search (Part I)
[ slides | video (zoom) ] |
|||
| 10/4 |
Lecture 8:
Neural Architecture Search (Part II)
[ slides | video | video (zoom) ] |
|||
| 10/6 |
Lecture 9:
Neural Architecture Search (Part III)
[ slides | video (zoom) ] |
Lab 1 due Paper review assignment out List of final projects out |
||
| 10/11 | Student Holiday — No Class | |||
| 10/13 |
Lecture 10:
Knowledge Distillation
[ slides | video | video (zoom) ] |
|||
| 10/18 |
Lecture 11:
MCUNet - Tiny Neural Network Design for Microcontrollers
[ slides | video | video (zoom) ] |
Lab 2 due | ||
| 10/20 |
Lecture 12:
Paper Reading Presentation
[ slides ] |
Paper reading presentation due | ||
| Efficient Training and System Support | ||||
| 10/25 |
Lecture 13:
Distributed Training and Gradient Compression (Part I)
[ slides | video | video (zoom) ] |
Submit project selection (or proposal) Midterm Evaluation Form out Lab 3 out |
||
| 10/27 |
Lecture 14:
Distributed Training and Gradient Compression (Part II)
[ slides | video | video (zoom) ] |
|||
| 11/1 |
Lecture 15:
On-Device Training and Transfer Learning (Part I)
[ slides | video | video (zoom) ] |
|||
| 11/3 |
Lecture 16:
On-Device Training and Transfer Learning (Part II)
[ slides | video | video (zoom) ] |
|||
| 11/8 |
Lecture 17:
TinyEngine - Efficient Training and Inference on Microcontrollers
[ slides | video | video (zoom) ] |
Lab 4 out |
||
| Application-Specific Optimizations | ||||
| 11/10 |
Lecture 18:
Efficient Point Cloud Recognition
[ slides | video | video (zoom) ] |
Lab 3 due | ||
| 11/15 |
Lecture 19:
Efficient Video Understanding and GANs
[ slides | video | video (zoom) ] |
|||
| 11/17 |
Lecture 20:
Efficient Transformers
[ slides | video | video (zoom) ] |
|||
| Quantum ML | ||||
| 11/22 |
Lecture 21:
Basics of Quantum Computing
[ slides | video | video (zoom) ] |
Lab 4 due | ||
| 11/24 | Thanksgiving — No Class | |||
| 11/29 |
Lecture 22:
Quantum Machine Learning
[ slides | video (zoom) ] |
|||
| 12/1 |
Lecture 23:
Noise Robust Quantum ML
[ slides | video (zoom) ] |
Final project presentation out |
||
| 12/6 |
Lecture 24:
Final Project Presentation
[ slides ] |
|||
| 12/8 |
Lecture 25:
Final Project Presentation
[ slides ] |
|||
| 12/13 |
Lecture 26:
Course Summary & Guest Lecture
[ slides | video | video (zoom) ] |
Final project report due | ||