• Offered by Research School of Engineering
  • ANU College ANU College of Engineering and Computer Science
  • Course subject Engineering
  • Academic career PGRD
  • Mode of delivery In Person
  • Offered in Second Semester 2017
    See Future Offerings

The course content will change from year to year depending on lecturer availability. Lecturers are drawn from the Computer Vision and Robotics and Systems and Control groups at the ANU including international academic visitors to these groups.

Learning Outcomes

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

Students will be able to demonstrate critical analysis of research and publications in an advanced specialist topic of current interest in mechatronics, and the current boundaries of knowledge and predominant research streams that are extending them.

Professional Skills Mapping
Mapping of Learning Outcomes to Assessment and Professional Competencies

Other Information

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

 1. The theory of Convolutional Neural Networks (CNNs).  The Universal Approximation Theorem.

 2. Theory of training a CNN, particularly the theory of Back Propagation and Stochastic Gradient Descent.

 3. Introduction to commonly used software for training and running CNNs, including in particular Matconvnet, a commonly used Matlab-based tool for Deep Learning.

 4. The course will be heavily practically oriented.  Students will be expected to have adequate programming and software skills.  A major part of the evaluation will be a final project, in which students are expected to obtain a dataset, train and run a CNN, related to an image-processing task.

Indicative Assessment

Assessment will be based on a combination of assignments, tutorials, labs and exams. Distribution of marks between assessment tasks will be determined by the course convener with details provided in the course outline at the start of semester.

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.


Will vary depending on specific topics.

Requisite and Incompatibility

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


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:
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.

6.00 0.12500
Domestic fee paying students
Year Fee
2017 $3660
International fee paying students
Year Fee
2017 $4878
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

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
Deep Learning Image Processing Computer Vision
8579 24 Jul 2017 31 Jul 2017 31 Aug 2017 27 Oct 2017 In Person N/A

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