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.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Review the fundamental concepts and techniques of the machine learning advanced topic.
- Reflect critically on the suitability of studied concepts and techniques for solving machine learning problems.
- Critically read research papers in the advanced topic area.
- Plan and execute project work and/or a piece of research and scholarship in the advanced topic.
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.
Indicative Assessment
- Assessments and their weighting will vary depending on the nature of the topic. Specific details will be presented in the Class Summary. (100) [LO 1,2,3,4]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
Lectures, tutorials and self study to a total of 130 Hours
Requisite and Incompatibility
Prescribed Texts
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.
Assumed Knowledge
- Familiarity with linear algebra (including norms, inner products, determinants, eigenvalues, eigenvectors, and singular value decomposition)
- Familiarity with basic probability theory
- Familiarity with multivariate differential calculus (e.g., derivative of a vector-valued function)
- Exposure to mathematical proofs
- Experience with Python programming
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.