- Code ENGN8535
- Unit Value 6 units
- Offered by RS Electrical, Energy and Materials Engineering
- ANU College ANU College of Engineering and Computer Science
- Course subject Engineering
- Academic career PGRD
- Dr Miaomiao Liu
- Mode of delivery In Person
First Semester 2019
See Future Offerings
Data-driven decision-making is an essential component of emerging engineering systems that generate and consume very large amounts of sensing data from autonomous vehicles to digital pathology. This course covers technologies and methodologies necessary for inferring useful information and identifying underlying patterns from often raw, incomplete, noisy and corrupted data that is present in real-life engineering applications. It will also give students the opportunity to explore advanced solutions of data analytics such as sparse encoding, compressive sensing, nonlocal filtering, discriminative and generative modelling.
Upon successful completion, students will have the knowledge and skills to:Upon successful completion of this course, students will be able to:
- Describe a number of models for supervised, unsupervised inference from data
- Assess the strength and weakness of each of these models
- Interpret the mathematical equations from Linear Algebra, Statistics, and Probability Theory used in the learning models
- Implement efficient learning algorithms on a computer
- Design test procedures in order to evaluate a model
- Combine several models in order to gain better results
Professional Skills Mapping
Mapping of Learning Outcomes to Assessment and Professional Competencies
Indicative AssessmentAssignments 30%, Project 40%, Final exam 30%
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Workload12 × 3 hr Lectures
Requisite and Incompatibility
Tuition fees are for the academic year indicated at the top of the page.
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- Student Contribution Band:
- Unit value:
- 6 units
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Offerings, Dates and Class Summary Links
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|
|3670||25 Feb 2019||04 Mar 2019||31 Mar 2019||31 May 2019||In Person||N/A|