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 SCA 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).

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Residential School No Residential School

Unit Availabilities from Term 3 - 2025

Term 3 - 2025 Profile
Online

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

This information will not be available until 8 weeks before term.
To see assessment details from an earlier availability, please search via a previous term.

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

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Previous Feedback

Term 2 - 2023 : The overall satisfaction for students in the last offering of this course was 100.00% (`Agree` and `Strongly Agree` responses), based on a 100% response rate.

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.

Source: Unit Coordinator
Feedback
Students wish to study basic techniques about the computer vision.
Recommendation
Add OpenCV basic tutorial materials and exercise.
Action Taken
A number of OpenCV application code has been added in tutorial classes.
Source: Teaching Evaluation
Feedback
It is better to use code to illustrate the lecture content.
Recommendation
For individual weeks, we can add Python code to explain relevant machine learning algorithms.
Action Taken
In Progress
Source: Self-Reflection
Feedback
The content can be further enriched by adding extended materials on image analysis using OpenCV libraries.
Recommendation
Adding appropriate new materials on using OpenCV for image analysis applications.
Action Taken
In Progress
Unit learning Outcomes
This information will not be available until 8 weeks before term.
To see Learning Outcomes from an earlier availability, please search via a previous term.