Overview

This topic provides a thorough exploration of selected supervised and unsupervised machine learning techniques with a working knowledge of Python for examples and assessment. Specific areas covered include: Data representation and feature engineering.Linear and logistic regression, K-means clustering, decision trees, random forests, and support vector machines.Methods of model evaluation and … For more content click the Read More button below.

Topic availabilities

To view topic availabilities, select an availability from the drop down, towards the top right of the screen.

Tuition pattern

To view tuition patterns, select an availability from the drop down, towards the top right of the screen.

Aims

This topic aims to provide a theoretical and practical introduction to the development and application of Machine Learning (ML) and Deep Learning (DL) systems. Students will understand the architectures and principles behind selected ML/DL models and gain hands-on experience in employing ML and DL techniques towards solving practical problems in a variety of domains. The scope of the topic incorporates increasingly relevant applications such computer vision, image analysis, data science and the ethical issues associated with these. Students will also obtain skills in analytical thinking, experimental research, and effective communication within the wider context of ML.

Learning outcomes

On completion of this topic you will be expected to be able to:
1.
Understand principles of ML and its capabilities
2.
Acquire practical skills to design, implement, train and evaluate ML models including DL systems.
3.
Utilise ML and DL systems towards solving a variety of relevant real-world problems.
4.
Evaluate and compare in a scientific manner the suitability and effectiveness of different ML techniques.
5.
Understand the practical and ethical constraints associated with ML systems.

Assessments

To view assessment information, select an availability from the drop down, towards the top right of the screen.

Current students should refer to FLO for detailed assessment information, including due dates. Assessment information is accurate at the time of publishing.

For policy details, visit Assessments

Requisites information

Pre-requisites:
Anti-requisites:

Assumed knowledge

Basic knowledge of computing such as can be obtained in COMP1001 Fundamentals of Computing or COMP1002 Fundamentals of Computational Intelligence or equivalent. Basic knowledge of programming in imperative and object-oriented languages such as Java and C/C++.