Teaching

I have been a Sessional Academic at n the School of Computing Technologies at RMIT University since 2022 for the following courses :

COSC2960: Foundations of Artificial Intelligence for STEM

Undergraduate course for students from diverse disciplinary backgrounds.

Teaching periods: 2024 (Semester 1, Flexible Term, Semester 2); 2025 (Semesters 1 and 2); 2026 (Semesters 1 and 2)

Average CES scores: Helpful: 3.79 | Inspired: 3.68

COSC2670: Practical Data Science with Python

Master/Bachelor of Data Science

Teaching periods: 2022 (Semester 1); 2023 (Semester 1and 2); 2024 (Semester 1and 2); 2025 (Semester 1); 2026 (Semester 1)

Average CES scores: Helpful: 3.77 | Inspired: 3.65

COSC2673: Computational Machine Learning

Master of Data Science and Artificial Intelligence

Teaching periods: 2026 (Semester 1)

Average CES scores: Helpful: 4.29 | Inspired: 3.57

COSC2669: Case Studies in Data Science

Master/Bachelor of Data Science

Teaching periods: 2024 (Semester 2)

Average CES score: Helpful: 4.70 | Inspired: 4.40

COSC2820 Advanced Programming for Data Science

Master of Data Science and Artificial Intelligence

Teaching periods: 2023 (Semester 2)

*CES scores reflect student feedback on how helpful and inspiring the learning experience was.

COSC2960: Foundations of Artificial Intelligence for STEM

The course. This course is designed for students from different disciplines to learn about and connect with recent advances in AI. It introduces AI, its history, basic classical AI methods, machine learning and data science. Most importantly, students complete several projects where they connect the learning outcomes to their own discipline.

My role. I started in this course as a tutor, running two-hour workshops that combined discussion with hands-on tutorials using real-world problems and data. A semester later, I became head tutor, helping with the assignments and with the content of the workshops and activities. After graduating, I worked as a lecturer and course coordinator, redesigning some learning activities to better align with the course objectives.

COSC2670: Practical Data Science with Python

This undergraduate and postgraduate course focuses on data analysis, preprocessing, and modelling. It introduces students to a range of machine learning algorithms and equips them with the practical skills needed to implement these techniques using Python. Students also develop proficiency in essential Python libraries, functions, and tools for data manipulation, analysis, visualisation, and machine learning, including scikit-learn, Pandas, NumPy and Matplotlib.

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