I guided Master's students in the Human-Centred AI program through the complexities of developing AI systems that are not only effective but also transparent, fair, and trustworthy. We explored key concepts like interpretability, bias mitigation, and responsible AI development, equipping students with the knowledge and skills to navigate the ethical challenges of AI in the real world.

Deep Learning 

In this course, I introduced undergraduate Data Science students to the foundational concepts and cutting-edge techniques of Deep Learning. We explored a wide range of topics, including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). 

Research Methods

This course equipped Master's students with the essential skills and knowledge to conduct rigorous and impactful research. We covered a wide range of research methodologies, including quantitative, qualitative, and mixed methods approaches. Students learned about research design, data collection techniques, data analysis methods, and ethical considerations in research. 

Engineering and Technology Project

This project-based course provided students with the opportunity to apply their engineering and technology knowledge to solve a real-world challenge. Working in teams, students engaged in all phases of the engineering design process, from problem identification and needs analysis to design, prototyping, testing, and evaluation.

Tools for Data Science

This course provided undergraduate students with a comprehensive overview of the essential tools and technologies used by data scientists. We explored a wide range of software, programming languages, and platforms, including Python, R, SQL, cloud computing platforms (e.g., AWS, Google Cloud), and big data technologies (e.g., Hadoop, Spark). 

Human-Centred Design