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
If you are an undergraduate student and 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). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
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|
|4155||19 Feb 2018||27 Feb 2018||31 Mar 2018||25 May 2018||In Person||N/A|