Teaching

Machine Learning (graduate level)

• Spring 2020, Fall 2019, 2020, 2021, 2022, 2023
• Topics: Bayesian Decision theory, MLE, Linear models (regression, classification), Non-parametric models, clustering, Neural Network, Probabilistic models, SVM, Boosting, etc.
• Recognition of teaching excellence (Fall 2021, Fall 2022)

Deep Learning (graduate level)

• Spring 2022, 2023
• Topics: Linear Regression/classification, Neural network (MLP), Convolutional neural networks, Recurrent neural networks, Attention, Machine translation, Generative models (VAEs, GANs), Robustness, etc.

Fundamental of Computing (graduate entry-level)

• Spring 2021
• Topics: Expression/variables, Function, Control flow, Recurrsion, String, List, Dictionary, Error handling, Loop, OOP, etc.