Dr Susan Wei, The University of Melbourne
Dr Pavel Krupskiy, The University of Melbourne
Dr Matthew Tam, The University of Melbourne
This course is intended to introduce the foundational theory of machine learning with a special focus on deep learning. Participants will be exposed to a range of tools that will be equally applicable in either academic or industry contexts. The intended learning outcome is that participants will be able to deploy deep learning in an end-to-end fashion and appreciate the theoretical mysteries that deep learning poses to classical statistical learning theory.
Week 1: The basics of machine learning using the linear neural networks as a guiding example
Week 2: Stochastic optimisation schemes which serve as the workhorse of modern deep learning
Week 3: How to design and train deep neural networks end-to-end on real datasets
Week 4: Classical statistical learning theory and its limitations in explaining the unreasonable effectiveness of deep learning.
Participation in all lectures and tutorials is expected.
For those completing the subject for their own knowledge/interest, evidence of at least 80% attendance at lectures and tutorials is required to receive a certificate of attendance.
Take this QUIZ to self-evaluate and get a measure of the key foundational knowledge required.
Susan Wei is a lecturer in the School of Mathematics and Statistics at the University of Melbourne. She currently holds a Discovery Early Career Researcher Award (DECRA) from the Australian Research Council (ARC) and a Visiting Faculty Researcher position at Google Deepmind in Sydney, Australia. Her research interests include statistics, machine learning, and deep learning. She is part of the Melbourne Deep Learning Group.
Pavel Krupskiy is a lecturer in the School of Mathematics and Statistics at Melbourne University. He got his PhD from the University of British Columbia (Canada) in 2014 and worked as a postdoctoral fellow at King Abdullah University of Science and Technology (Saudi Arabia) in 2015-2017 and University of British Columbia in 2017-2018. His research interests include copula models for multivariate data, multivariate extremes, spatial data modeling and nonparametric statistics.
After receiving a PhD from the University of Newcastle in 2016, Matthew Tam moved to the University of Göttingen where he was a post-doctoral researcher supported by the RTG-2088 “Discovering structure in complex data: Statistics meets Optimization and Inverse Problems” and the Alexander von Humboldt Foundation. He subsequently joined the faculty at the University of Göttingen after being appointed Junior Professor for Mathematical Optimisation. In 2020, he returned to Australia, joining the School of Mathematics and Statistics at the University of Melbourne.