# Teaching

## Tutoring Experience

I am currently tutor within the School of Mathematics and Statistics and the School of Computing and Information Systems at the University of Melbourne.

**COMP30026 Models of Computation**(2023 Sem. 2, Tutor) A third-year computer science subject. Topics include logic, automata theory, computability, discrete mathematics. Uses the Haskell programming language.**MAST20029 Engineering Mathematics**(2023 Sem. 2, Tutor) A second-year subject covering a variety of topics in vector calculus and differential equations.**MAST10005 Calculus 1**(2023 Sem. 2, Tutor) A introductory course in calculus.**COMP30024 Artificial Intelligence**(2023 Sem. 1, Tutor): A third-year computer science subject. Topics include search, game playing, auction design and constraint satisfaction problems. Uses the Python programming language.**MAST10007 Linear Algebra**(2023 Sem. 2, Tutor) An introductory course in linear algebra.**MAST10005 Calculus 1**(2023 Sem. 1, Tutor)**COMP30026 Models of Computation**(2022 Sem. 2, Tutor)**MAST10007 Linear Algebra**(2022 Sem. 2, Tutor)**COMP30024 Artificial Intelligence**(2022 Sem. 1, Tutor)**MAST10007 Linear Algebra**(2022 Sem. 1, Tutor)**COMP30026 Models of Computation**(2021 Sem. 2, Tutor)**MAST10007 Linear Algebra**(2021 Sem. 2, Tutor)**COMP30024 Artificial Intelligence**(2021 Sem. 1, Tutor)**MAST10005 Calculus 1**(2021 Sem. 1, Tutor)**COMP30026 Models of Computation**(2020 Sem. 1, Tutor)**COMP10001 Foundations of Computing**(2018 Sem. 1, Demonstrator) An introductory course in programming, in Python.

## Seminars

This is a list of seminars I have organised. These seminars were designed for first and second year mathematics students. My responsibilities included designing the seminars’ curriculum, mentoring students preparing for their talks and general seminar administration.

### Neural Networks (2021 Sem. 1)

Co-organised with Nora Ganter.

Topics:

*Introduction:*What is machine learning? Logistic regression.*Defining neural networks:*What is a neural network? Explain the need for activation functions and state approximation results.*Training neural networks:*Discuss gradient descent and backpropagation.*GANs part 1:*Discuss the standard GAN formulation.*GANs part 2:*Discuss the Wasserstein GAN, as introduced in Arjovsky, Chintala, Bottou, “Wasserstein GAN” arXiv:1701.07875.

Texts:

- Nielsen (2015)
*Neural Networks and Deep Learning*. Determination Press, http://neuralnetworksanddeeplearning.com - Liquet, Moka, Nazarathy (2020)
*The Mathematical Engineering of Deep Learning*. https://deeplearningmath.org - Goodfellow, Bengio, Courville (2016)
*Deep learning*. Cambridge, Massachusetts: MIT Press. - Arjovsky, Chintala, Bottou (2017) “Wasserstein GAN” arXiv:1701.07875.

### Game Theory (2020 Sem. 2)

Co-organised with Nora Ganter, Jonah Nelson, Kshitija Vaidya, and Chengjing Zhang.

Topics:

*Introduction to games**Nash equilibrium**Compactness in*\(\mathbb{R}^n\)*Kakutani’s Theorem**Zero-sum games**Minmax Theorem**Solution concepts*

Texts:

- Fudenberg, Tirole (1991)
*Game theory.*Cambridge, Massachusetts: MIT Press. - Leyton-Brown, Shoham (2008)
*Essentials of game theory: a concise, multidisciplinary introduction.*San Rafael, California: Morgan & Claypool Publishers.

### Foundations of Mathematical Cryptography (2020 Sem. 1)

Co-organised with Majid Alamudi, Nora Ganter, Adam Walsh, Chengjing Zhang and Gufang Zhao.

Topics:

*Introduction to proofs**Binomial coefficients**Modular arithmetic, Euclidean algorithm, Bezout’s Lemma**Prime factorisation**RSA**Groups and cosets**Discrete logarithm part 1**Discrete logarithm part 2*