Teaching

Introductory machine learning course covering Bayesian learning, regression, classification, clustering, dimensionality reduction, etc. (own design)

Graduate machine learning course covering graphical models, graph neural networks, deep generative models, agentic AI systems, etc. (own design)

Classical undergraduate course covering liner time-invariant systems, Laplace transform, Fourier series, Fourier transform, etc.