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School of Computing · ANU College of Systems and Society
This course builds on previous machine learning (ML) courses to explore selected areas relevant to machine learning in depth. It will be taught by an ML staff member of internationally recognised standing and research interest in that area. Based on current ML staffing, this will be one or more of: - decision making in robotics - kernel methods - structured probabilistic models - reinforcement learning - convex analysis and optimisation - topics in information theory - decision theory - deep learning - differentiable optimisation in deep learning Students should contact the course convenor to find out what topic is planned for the coming semester.
Assessments and their weighting will vary depending on the nature of the topic. Specific details will be presented in the Class Summary.
Learning outcomes 1, 2, 3, 4
Lectures, tutorials and self study to a total of 130 Hours Other Information The final assessment will be confirmed in the class summary dependant upon the area of machine learning that will be covered in this Advanced topic course.
Will be advised via the Class Summary Preliminary Reading 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) - H. Choset, K.M. Lynch, S. Hutchinson, G.A. Kantor, W. Burgard, L.E. Kavraki and S. Thrun. Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press. 2005 (representations for sequential decision-making in robotics and robot planning) - Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press 2018 (reinforcement learning) - Kyle H. Wray, Mykel J. Kochenderfer, Tim A. Wheeler. Algorithms for Decision Making, MIT Press 2022 (sequential decision-making) 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. 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.