An Introduction to Machine Learning

In this talk Daniel will provide an overview of machine learning, a fast-growing and increasingly pervasive branch of artificial intelligence. From financial trading to supermarket checkouts, machine learning techniques have become a powerful and efficient tool for processing, extracting and acting on insights from big datasets. Illustrated with examples from industry and academia, Daniel will address key applications of machine learning, spanning language, vision, reinforcement learning and human-machine interaction.

Daniel is a roboticist with interests encompassing human-machine interaction, control engineering and language technology. His qualifications include a Master of Engineering in Electrical and Electronic Engineering, (Imperial College London, 2019) and a Master of Science in Machine Learning (University College London, 2020). He is currently undertaking doctoral research with the Control and Signal Processing Group at the University of Melbourne, focusing on human interactions with robotic swarms.