- 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
- Areas of interest Statistics, Algorithms and Data, Artifical Intelligence
- Academic career PGRD
- Prof Hongdong Li
- Mode of delivery In Person
First Semester 2020
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 applications. It will also give students the opportunity to explore advanced solutions of data analytics such as dimensionality reduction, sparse encoding, compressive sensing, nonlinear filtering, manifold learning, and generative data modelling.
Upon successful completion, students will have the knowledge and skills 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
See Wattle page
- Assignments (45) [LO null]
- Mid-Term Quiz (25) [LO null]
- Final Exam (30) [LO null]
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
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12 x 2 hr Lectures
12 x1 hr Tutorial
Requisite and Incompatibility
"Element of statistical machine Learning"
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 start date
|Last day to enrol
|Class end date
|Mode Of Delivery
|24 Feb 2020
|02 Mar 2020
|08 May 2020
|05 Jun 2020