## Undergraduate level

### Introduction to Machine Learning

See an introductory lecture on machine learning here

### Deep Learning and Computer Vision

I am teaching the undergraduate level course “Deep Learning and Computer Vision” this 2021 Fall semester, together with Prof. Xinggang Wang (https://xinggangw.info), below are the assignments/mini-projects aiming to improve your theoretical understanding and practical (coding) skills.

- mini-project 1: training a linear model with gradient descent, see description here
- mini-project 2: training a single-hidden-layer neural network model, see description here
- mini-project 3: training a convolutional neural network, see description here
- mini-project 4: build your own MNIST-GAN, see description here

## Graduate level

### Probability and Stochastic Processes I

I am teaching the graduate (and Ph.D.!) level source “Probability and Stochastic Processes I” this 2022 Autumn semester, with Prof. Kai Wan, see slides here: PSP-V, PSP-VI, and PSP-VII.

### Probability and Stochastic Processes II (Random Matrix Theory and Its Application in Large-Scale Systems)

I am teaching the graduate (and Ph.D.!) level source “Probability and Stochastic Processes II” this 2022 Spring semester, with a focus on Random Matrix Theory and Its Application in Large-Scale Systems, together with Prof. Tiebin Min and Prof. Caiming Qiu. Slides:

- Lecture 0: Introduction in Chinese
- Lecture 1: Introduction
- Lecture 2: RMT basis: from random scalars to random matrices
- Lecture 3: MP and semicircle laws
- Lecture 4: Large-dimensional sample covariance matrix
- Lecture 4: Spiked model
- Course final project