COIT29225 - Neural Networks and Deep Learning

General Information

Unit Synopsis

Artificial Intelligence (AI) is becoming an important part of software development. Neural networks and Deep Learning are the main contributors to the recent advances in applications of Artificial Intelligence. Deep Learning enables computers to learn complicated concepts by building them out of a hierarchy of simpler ones. Deep Learning techniques have been successfully applied to a broad field of applications such as computer vision, image and video recognition, natural language processing, and medical diagnosis. This unit introduces you to the fundamentals of Deep Learning and how it can solve problems in many areas. In this unit, you will learn the architecture of neural networks and algorithms, including the latest Deep Learning techniques. You will learn to develop conventional neural networks such as multilayer perceptrons, and convolutional neural networks. You will use software to train and deploy neural networks. You will also identify practical applications of Deep learning by exploring recent case studies.

Details

Level Postgraduate
Unit Level 9
Credit Points 6
Student Contribution Band 2
Fraction of Full-Time Student Load 0.125
Pre-requisites or Co-requisites

Pre-requisite: COIT20277 Introduction to Artificial Intelligence

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

Class Timetable View Unit Timetable
Residential School No Residential School

Unit Availabilities from Term 1 - 2020

Term 2 - 2020 Profile
Brisbane
Melbourne
Online
Sydney
Term 1 - 2021 Profile
Brisbane
Melbourne
Online
Sydney
Term 2 - 2021 Profile
Brisbane
Melbourne
Online
Sydney

Attendance Requirements

All on-campus students are expected to attend scheduled classes – in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).

Assessment Overview

Recommended Student Time Commitment

Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.

Assessment Tasks

Assessment Task Weighting
1. Practical Assessment 30%
2. Practical Assessment 35%
3. Project (applied) 35%

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%).

Consult the University’s Grades and Results Policy for more details of interim results and final grades

Past Exams

To view Past Exams, please login
Previous Feedback

No previous feedback available

Feedback, Recommendations and Responses

Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.

Unit learning Outcomes

On successful completion of this unit, you will be able to:

  1. Formulate a neural network and deep learning problem applying the concepts and theory of classical and deep learning techniques
  2. Design deep learning solutions to problems in pattern recognition and image analysis
  3. Build a software application implementing neural networks using a high level programming language
  4. Evaluate the performance of the deep learning techniques used in the software application
  5. Investigate the application of intelligent systems in socially innovative applications.

The Skills Framework for the Information Age (SFIA) standard covers the skills and competencies related to information and communication technologies. SFIA defines levels of responsibility and skills. SFIA is adopted by organisations, governments and individuals in many countries. SFIA is increasingly being used when developing job descriptions and role profiles. SFIA can be used by individuals for creating personal skills profile. The Australian Computer Society (ACS) recognises the SFIA and provides MySFIA for ACS members to build a skills profile.

This unit contributes to the following workplace skills as defined by SFIA 7 (the SFIA code is included):

Software design (SWDN)

Programming/software development (PROG)

Testing (TEST)

Application Support (ASUP).

Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4 5
1 - Practical Assessment
2 - Practical Assessment
3 - Project (applied)
Alignment of Graduate Attributes to Learning Outcomes
Advanced Level
Professional Level
Graduate Attributes Learning Outcomes
1 2 3 4 5
1 - Knowledge
2 - Communication
3 - Cognitive, technical and creative skills
4 - Research
5 - Self-management
6 - Ethical and Professional Responsibility
7 - Leadership
Alignment of Assessment Tasks to Graduate Attributes
Advanced Level
Professional Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7
1 - Practical Assessment
2 - Practical Assessment
3 - Project (applied)