• Offered by RS Electrical, Energy and Materials Engineering
  • ANU College ANU College of Engineering and Computer Science
  • Course subject Engineering
  • Academic career PGRD
  • Course convener
    • AsPr Nicholas Barnes
  • Mode of delivery In Person
  • Offered in Second Semester 2019
    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. Design and train neural networks to mimic simple logical operations and solve simple applications of such logical problems.
  4. Competently use major software tools in the area of deep neural networks including matconvnet and a matlab based tool for deep learning.
  5. Apply programming and software tools to solve realistic problems of image processing using deep neural networks.
  6. Write programs to design and test neural networks when applied to complex logical tasks.
  7. Research an area of image processing and apply deep neural network methods to a problem in that area.

Indicative Assessment

  1. Problem sets (20) [LO null]
  2. Assignments (30) [LO null]
  3. Project Report with Oral presentation (50) [LO null]

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Workload

12 × 2 hr Lectures, 11 × 2 hr Tutorials

Inherent Requirements

Not applicable

Requisite and Incompatibility

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

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
2019 $4320
International fee paying students
Year Fee
2019 $5700
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
8255 22 Jul 2019 29 Jul 2019 31 Aug 2019 25 Oct 2019 In Person N/A

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