Teaching

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 II

I am teaching the graduate (and Ph.D.) level source “Probability and Stochastic Processes II,” with Dr. Zenan Ling, see slides here:

• Stochastic convergence
• Decision
• Estimation part 1
• Estimation part 2

Convex Optimization

• An introductory lecture on non-convex optimization