Machine Learning is about developing systems that automatically improve their performance through experience. It has found applications in many AI systems and products. Examples include systems that recommend online videos, automatic translating languages, and autonomous driving vehicles. This course focus on a sub-field of machine learning -- Deep Learning, with moderate introduction to general learning concepts and methods. We cover topics such as supervised learning, unsupervised learning, self-supervised learning, and various neural network architectures including convolutional neural networks, recurrent neural networks, Transformer, and graph neural networks. We will cover techniques for designing loss, training and inference methods. We focus on both the principles, analytical skills and implementation practice. This course is suitable for undergraduate students and graduate students who wants to pursue career in AI or do research in machine learning.
Lei Li
Office Hour: Monday 4-5pm (on zoom)
Monday/Wednesday 11:00am-12:15pm On Zoom for the first two weeks (if in-person teaching, at NH 1006)
TA sessions on Wednesday (use the same zoom)
Prerequisites: Students need to grasp knowledge in Linear algebra, Calculus, Probability and Statistics, basic algorithms, and significant experience in computer programming (python, C++, or Java).
CS 130A&130B, MATH 3B, 6A, PSTAT 120A, 120B.
We will use Ed platform. Please signup here.
Please read the following Link
carefully!
| # |
Date |
Topic |
Reading |
Homework |
| 1 |
1/3 |
Introduction |
Chap 1 of GBC |
HW1 |
| 2 |
1/5 |
Linear Models, Vector Calculus |
Chap 5 of GBC |
|
| 1/5 | Recitation: slide1, slides2 | |||
| 3 |
1/10 |
Logistic Regression, Cross
Entropy |
Chap 5 of GBC |
|
| 4 |
1/12 |
Feedforward Network, Empirical Risk
Minimization, Gradient Descent |
Chap 6 of GBC |
HW1 due, HW2 out |
| 1/12 | Recitation: slide, note | |||
| 1/17 | holiday. no class | |||
| 5 |
1/19 |
Backpropagation and Autograd |
||
| 1/19 | Recitation: | |||
| 6 |
1/24 |
Evaluation and Regularization | Chap 7 of GBC |
|
| 7 |
1/26 |
Convolutional Neural Network | Chap 9 of GBC |
|
| 8 |
1/31 |
ResNet and other CNN variants | HW2 Due, HW3 out | |
| 9 |
2/2 |
Learning for CNN |
||
| 10 |
2/7 |
Optimization for ML |
||
| 11 |
2/9 |
Object Detection |
||
| 12 |
2/14 |
Recurrent Neural Networks
|
||
| 13 |
2/16 |
Sequence-to-sequence Models | HW3 Due, HW4 out | |
| 2/21 |
holiday. no class |
|||
| 14 |
2/23 |
Transformer | ||
| 15 |
2/28 |
NLP Pretraining | ||
| 16 |
3/2 |
Graph Neural Network | ||
| 17 |
3/7 |
Autoencoder and VAE | HW4 due on 3/8 |
|
| 18 |
3/9 |
GAN | ||
| 3/17 12pm |
Final Exam |