Part A: Course Overview

Course Title: Deep Learning

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2779

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 2 2020,
Sem 2 2021

COSC2779

City Campus

Postgraduate

175H Computing Technologies

Face-to-Face

 Sem 2 2022,
Sem 2 2023,
Sem 2 2024

COSC3007

RMIT University Vietnam

Postgraduate

175H Computing Technologies

Face-to-Face

 Viet3 2023,
Viet3 2024

Course Coordinator: Dr Ruwan Tennakoon

Course Coordinator Phone: +61 3 9925 3306

Course Coordinator Email: ruwan.tennakoon@rmit.edu.au

Course Coordinator Location: 14.11.03

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-Requisite Courses

Successful completion of the following course/s:

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.

For information go to RMIT Course Requisites webpage.

 

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer.

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.

Please follow the link for further information on how to apply for credit for prior study or experience.


Course Description

Deep Learning is a field of Machine Learning that focus on large scale neural networks. Deep Networks are suited for solving a variety of complex problems, such as computer vision, natural language processing, and large-scale state estimation. While Deep Networks require vast collections of training data, the networks often outperform humans. Deep Learning has increasingly become a core aspect of both major IT companies and new tech start-ups.

You will undertake a thorough study of the Deep Learning, from its foundations in perceptron’s and multi-layer networks, through to present-day deep architectures including convolutional neural networks. You will critically analyse issues with deep learning, learn how to use open source toolkits, and learn how to critically analyse outputs from these applications. Assessed work will involve real world datasets from a variety of domains.


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the program learning outcomes for the following program(s):

MC271 - Master of Artificial Intelligence

PLO 1    Enabling Knowledge
PLO 2    Critical Analysis
PLO 3    Problem Solving

For more information on the program learning outcomes for your program, please see the program guide.


Upon successful completion of this course you should be able to:

  1. Discuss and critically analyse a variety of neural network architectures; Evaluate and Compare approaches and algorithms on the basis of the nature of the problem/task being addressed.
  2. Synthesise suitable solutions to address particular machine learning problems based on analysis of the problemand characteristics of the data involved.
  3. Communicate effectively with a variety of audiences through a range of modes and media, in particular to: interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.
  4. Develop skills for further self-directed learning in the general context of neural networks and machine learning; Research, Discuss, and Use new and novel algorithms for solving problems; Adapt experience and knowledge to and from other computer sciences contexts such as artificial intelligence, machine learning, and software design.


Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both.

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course.


Overview of Learning Resources

You are encouraged to bring your laptops and use the freely available software to conduct the laboratories.

You will make extensive use of computer laboratories and relevant software provided by the University. You will be able to access course information and learning materials through MyRMIT and Canvas. Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.


Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

Assessment Task 1: Assignment 1
Weighting 30%
This task supports CLOs: 1, 2, 4

Assessment Task 2:  Major project
Weighting 50%
This task supports CLOs: 1-4 

Assessment Task 3: Presentation
Weighting 20%
This task supports CLOs: 1-4

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.