# Teaching

Materials for courses I have taught.

### 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