Useful links
This course provides an introduction to deep machine learning, which is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of applications. Students taking this course will be exposed to cutting-edge research in machine learning, starting from theories, models, and algorithms, to implementation and recent progress of deep learning. Specific topics include: classical architectures of deep neural network, optimization techniques for training deep neural networks, theoretical understanding of deep learning, and diverse applications of deep learning in computer vision.
Study level | Undergraduate |
---|---|
Academic unit | Computer Science |
Credit points | 6 |
Prerequisites:
?
|
(DATA3888 or COMP3888 or COMP3988 or CSEC3888 or SOFT3888 or ENGG3112 or SCPU3001) and (COMP3308 or COMP3608 or COMP4318 or BMET2925) |
---|---|
Corequisites:
?
|
None |
Prohibitions:
?
|
COMP5329 or OCMP5329 |
Assumed knowledge:
?
|
A major in a computer science area |
At the completion of this unit, you should be able to:
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
Session | MoA ? | Location | Outline ? |
---|---|---|---|
Semester 1 2024
|
Normal evening | Camperdown/Darlington, Sydney |
View
|
Session | MoA ? | Location | Outline ? |
---|---|---|---|
Semester 1 2025
|
Normal evening | Camperdown/Darlington, Sydney |
Outline unavailable
|
Find your current year census dates
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.