- Code ENGN8536
- Unit Value 6 units
- Offered by School of Engineering
- ANU College ANU College of Engineering and Computer Science
- Course subject Engineering
- Areas of interest Engineering
- Academic career PGRD
- Dr Tim Molloy
- Mode of delivery In Person
Second Semester 2022
See Future Offerings
In Sem 2 2022, this course is delivered on campus with adjustments for remote participation due to unavoidable COVID constraints.
This advanced graduate course explores Bayesian inference as a core enabling technology for modern, increasingly autonomous, mechatronic systems. The course introduces the conceptual and mathematical underpinnings of Bayesian inference with a particular emphasis on its ability to integrate model-based prior knowledge about dynamical systems with information derived from sensors and perception systems. Algorithms for Bayesian inference commonly employed in mechatronic systems will be examined in detail including Bayesian filters, Bayesian smoothers, and Bayesian sequential detection algorithms. The theory and algorithms of Bayesian inference will be applied to problems in mechatronics such as localisation and mapping for mobile robots, sensor fusion and moving target tracking for autonomous vehicles, and fault detection for industry 4.0+.
Upon successful completion, students will have the knowledge and skills to:
- Explain the mathematical foundations of Bayesian inference including the relationship between prior and posterior probabilities, and likelihoods.
- Derive recursive forms of the hidden Markov model filter and the Kalman filter, and discuss the impact of model parameters on their performance.
- Evaluate and compare Bayesian inference algorithms including their theoretical optimality properties.
- Employ factor-graph formulations and numerical methods to solve large-scale Bayesian inference problems.
- Design and implement Bayesian inference algorithms for mechatronic applications such as localisation and mapping, sensor fusion, target tracking, and fault detection.
- Problem sets (10) [LO 2,3,4]
- Project (40) [LO 3,4,5]
- Final Exam (50) [LO 1,2,3,5]
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120 hours in total over the semester consisting of:
- 12 × 2 hr Lectures/week
- 11 × 2 hr Tutorials/week
- Self-directed learning as required.
Information on inherent requirements for this course is currently not available.
Requisite and Incompatibility
- S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT Press, 2005.
- S. Särkkä, Bayesian Filtering and Smoothing, Cambridge University Press, 2013.
- V. Krishnamurthy, Partially Observed Markov Decision Processes, Cambridge University Press, 2016.
Mathematics including differential equations, probability theory, statistics, and matrix analysis. Students are also required to have adequate programming and software skills.
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:
- 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.
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- Domestic fee paying students
- International fee paying students
Offerings, Dates and Class Summary Links
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|5569||25 Jul 2022||01 Aug 2022||31 Aug 2022||28 Oct 2022||In Person||View|