• Offered by RS Electrical, Energy and Materials Engineering
  • ANU College ANU College of Engineering and Computer Science
  • Course subject Engineering
  • Areas of interest Engineering
  • Academic career PGRD
  • Course convener
    • AsPr Nicholas Barnes
  • Mode of delivery In Person
  • Offered in Second Semester 2020
    See Future Offerings

The course will describe the theory and practice of deep Neural Networks, otherwise known as Deep Learning, with a particular emphasis on their use in Image Processing and Computer Vision.

Learning Outcomes

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

  1. Describe and apply the theory of convolutional neural networks and the universal approximation theorem.
  2. Apply the theory of training a convolutional neural network.
  3. Write programs to design, train and test neural networks to solve problems in computer vision.
  4. Competently apply programming and software tools to write programs to solve realistic problems of computer vision using deep neural networks.
  5. Understand the principals of testing and evaluating the performance of deep neural networks, and apply these principals in practice.
  6. Research an area of computer vision and apply deep neural network methods to a problem in that area.

Indicative Assessment

  1. Problem sets (20) [LO 1,2,3,4,5,6]
  2. Assignments (30) [LO 1,2,3,4,5,6]
  3. Examinations (50) [LO 1,2,3,4,5,6]

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 × 2 hr Lectures, 11 × 2 hr Tutorials

Inherent Requirements

Information on inherent requirements for this course is currently not available.

Requisite and Incompatibility

To enrol in this course you must be studying Master of Engineering or Master of Machine Learning and Computer Vision.

Prescribed Texts

None

Assumed Knowledge

Mathematics including differential equations, probability theory, statistics, and matrix analysis. Students are also required to have adequate programming and software skills.

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.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
7819 27 Jul 2020 03 Aug 2020 31 Aug 2020 30 Oct 2020 In Person N/A

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