Teaching

CE7454 Deep Learning for Data Science

This is a graduate-level course that serves as an (advanced) introduction to deep learning. I co-teach this course with Prof. Liu Ziwei. My part covers the following topics in 7 weeks:

1. Introductory Linear Algebra
2. Introductory Probability Theory
3. Basic Machine Learning Models (Linear Regression, Logistic Regression, Multi-layer Perceptron)
4. Convolutional Neural Network and Major Variants (ResNet, DenseNet, MobileNet, EfficientNet, Grouped Convolution, 3D Convolution, Temporal Convolution, etc.)
5. Optimization of Neural Networks. [Slides]
6. Regularization of Neural Networks.

AI6103 Deep Learning and Applications

This is a 13-week course for the Master of Science in Artificial Intelligence (MSAI) program. It covers most topics of CE7454. Additionally, it covers recent advances in large language models, as well as some programming in PyTorch.

SC4000 Machine Learning

This is an undergraduate-level course for machine learning, which I co-teach with Prof. Ke Yiping, Kelly. The course covers a wide range of topics, including artificial neural networks, support vector machines, clustering, dimensionality reduction, and so on.