teaching

Introductory yet rigorous course covering Bayesian modeling and inference (priors/posteriors), MCMC, Laplace approximation, and variational inference.

Taught the Large Language Models module: foundations, architectures, and hands-on implementation. Participants built an LLM agent using JAX and Haiku.

Topics: supervised/unsupervised learning, deep learning for structured & image data, sequence modeling, and reinforcement learning applications for IoT.

In-depth workshop on Apache Airflow. Students learned how to design, schedule, and monitor production-grade data workflows using DAGs and custom operators.