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:
- Describe and apply the theory of convolutional neural networks and the universal approximation theorem.
- Apply the theory of training a convolutional neural network.
- Write programs to design, train and test neural networks to solve problems in computer vision.
- Competently apply programming and software tools to write programs to solve realistic problems of computer vision using deep neural networks.
- Understand the principals of testing and evaluating the performance of deep neural networks, and apply these principals in practice.
- Research an area of computer vision and apply deep neural network methods to a problem in that area.
Indicative Assessment
- Problem sets (20) [LO 1,2,3,4,5,6]
- Assignments (30) [LO 1,2,3,4,5,6]
- Examinations (50) [LO 1,2,3,4,5,6]
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.
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
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 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2020 | $4320 |
- International fee paying students
| Year | Fee |
|---|---|
| 2020 | $5760 |
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.
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 |
