Teaching

Probabilistic Artificial Intelligence

I wrote the lecture notes covering probabilistic machine learning (probabilistic inference, Bayesian linear regression, Gaussian processes, variational inference, MCMC, Bayesian deep learning) and sequential decision-making (active learning, Bayesian optimization, MDPs, reinforcement learning). Additionally, I designed homeworks, held tutorial classes, and designed exam questions.

Teaching Assistant: fall 2022, fall 2023

Introduction to Machine Learning

I headed the exam design of ETH’s largest class with ~1000 students.

Teaching Assistant: spring 2024

Advanced Graph Algorithms and Optimization

I held tutorial classes and wrote a summary of parts of the lecture notes.

Teaching Assistant: spring 2023

Algorithms and Probability

I held a weekly tutorial class and graded homeworks.

Teaching Assistant: spring 2022

Discrete Probability Theory

I held weekly tutorial classes and teached a 5-day revision course covering discrete and continuous probability spaces, random variables, inductive statistics, and Markov chains.

Teaching Assistant: summer 2020, summer 2021

Theory of Computation

I held a weekly tutorial class and graded homeworks.

Teaching Assistant: summer 2021

Functional Programming and Verification

I held a weekly tutorial class and teached a 4-day revision course covering basics of Haskell, proofs of correctness by structural/computation induction and abstraction functions, I/O and monads, evaluation order.

Teaching Assistant: winter 2019, winter 2020