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 CZ/CE 7454. Additionally, it covers structured predictions in both computer vision and natural language, 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.