• 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
  • Course convener
    • Prof Hongdong Li
  • Mode of delivery In Person
  • Offered in 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.

 

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Describe a number of models for supervised, unsupervised inference from data
  2. Assess the strength and weakness of each of these models
  3. Interpret the mathematical equations from Linear Algebra, Statistics, and Probability Theory used in the learning models
  4. Implement efficient learning algorithms on a computer
  5. Design test procedures in order to evaluate a model
  6. Combine several models in order to gain better results

Other Information

See Wattle page

Indicative Assessment

  1. Assignments (45) [LO null]
  2. Mid-Term Quiz (25) [LO null]
  3. 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 via Wattle and/or students should have been advised by the offering College. 

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Workload

12 x 2 hr Lectures

12 x1 hr Tutorial

Homework Assignments

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course you must be studying Master of Engineering.

Prescribed Texts

N/A

Preliminary Reading

"Pattern Recognition"

"Element of statistical machine Learning"

"Data science"

"Big Data"



Assumed Knowledge

Linear algebra,

Probability

Statistics

Computer programming



Fees

Tuition fees are for the academic year indicated at the top of the page.  

If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Student Contribution Band:
2
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.

Units EFTSL
6.00 0.12500
Domestic fee paying students
Year Fee
2020 $4320
International fee paying students
Year Fee
2020 $5760
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

First Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
3075 24 Feb 2020 02 Mar 2020 08 May 2020 05 Jun 2020 In Person N/A

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions