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School of Computing · ANU College of Systems and Society
This course explores a selected area relevant to statistical machine learning in depth, and will be taught by an SML staff member of internationally recognised standing and research interest in that area. Based on current SML staffing, this will be one of: kernel methods structured probabilistic models reinforcement learning convex analysis and optimisation topics in information theory decision theory deep learning differentiable optimisation in deep learning Over the past several years the content has alternated between “convex analysis and optimisation” and “differentiable optimisation in deep learning". Students should contact the course convenor to find out what topic is planned for the coming semester.
Regular homework assignments
Learning outcomes 1, 2, 3, 4, 5, 6, 7, 8
Open-book final exam
Learning outcomes 1, 2, 3, 4, 5, 6, 7
Lectures, Tutorials and self study to a total of 130 hours.
Main text (depending on the topic taught): - Stephen Boyd and Lieven Vandenberghe, "Convex Optimization", Cambridge Press, 2004 (convex optimisation) - Ian Goodfellow, Yoshua Bengio and Aaron Courville, "Deep Learning", MIT Press, 2016 (deep learning) - Kevin Murphy, "Machine Learning", MIT Press (structured probabilistic models) Reference texts: - Rockafellar, "Convex Analysis", Princeton Press. - Hiriart-Urruty and Lemarechal, “Fundamentals of Convex Analysis”, Springer. - Bertsekas, Nedic and Ozdaglar, “Convex Analysis and Optimization”, Athena Scientific. - Bertsekas, “Nonlinear Programming”, Athena Scientific. - Koller and Friedman, "Probabilistic Graphical Models", MIT Press. - Bishop, "Pattern Recognition and Machine Learning", Springer. Preliminary Reading Background texts: - Strang, "Introduction to Linear Algebra", Cambridge Press. - Garrity, "All the Mathematics You Missed: But Need to Know for Graduate School", Cambridge University Press.