This course comprehensively explores how advanced deep learning techniques can be applied to solve various challenges in electronic engineering. It examines the use of neural networks for core tasks like signal detection, recognition, analysis, prediction, synthesis, and generation. It explores how deep learning can enhance the understanding, manipulation, and even the creation of electronic signals and data, providing students with a robust foundation in this rapidly evolving intersection of artificial intelligence and electronic engineering.
The course begins by characterising electronic signals and systems across various dimensions and outlines core engineering tasks related to signal and sensor data. It then introduces machine learning principles that form the basis of deep learning and continues with the following topics: discriminative deep learning models for both multi-dimensional signal analysis and sequential data processing, generative deep learning methods for signal enhancement and synthesis, emerging applications of deep learning in electronic systems, including the integration of physical principles through Physics-Informed Neural Networks, concluding with a forward-looking perspective on the field's future directions.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Interpret electronic signals and systems with respect to variations in time, frequency, space, and other dimensions, and formulate tasks such as detection, recognition, analysis, prediction, synthesis, and generation.
- Employ discriminative deep learning methods, such as CNNs, RNN variants and Transformers to process electronically acquired information.
- Utilize generative deep learning techniques, such as autoencoders and diffusion models, for electronic system applications such as denoising, compression, and signal synthesis.
- Choose programming languages and libraries relevant to deep learning to build, train, optimise, and evaluate various models applicable to electronic engineering tasks.
- Demonstrate an understanding of current trends in deep learning and future directions applicable to electronic systems.
- Effectively communicate the capabilities and limitations of AI solutions in electronic systems to a diverse audience using concise technical reports and/or presentations.
Indicative Assessment
- Assignments (30) [LO 1,2,3,4,5,6]
- Labs (30) [LO 1,2,3,4,6]
- Project (40) [LO 2,3,4,5,6]
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Workload
Approximately 10 hours per week is expected with a total of 130 hours for the entire course. The workload covers the learning activities of lecture content, labs and project, as well as adequate self-study and completion of assessments.
Requisite and Incompatibility
Prescribed Texts
None
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.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 10140 | 26 Jul 2027 | 02 Aug 2027 | 31 Aug 2027 | 29 Oct 2027 | In Person | N/A |