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.

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Tuition pattern

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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 as 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.
Utilise principles of ML and understand its capabilities and limitations
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 analyse in a scientific manner the suitability and effectiveness of different ML techniques
5.
Identify the practical and ethical constraints associated with ML systems
6.
Conduct independent individual studies in applications of ML, extending those covered in the topic, with the level of understanding allowing for practical applications of the material

Assessments

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

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Requisites information

Pre-requisites:
Anti-requisites:

Assumed knowledge

Basic knowledge of computing such as can be obtained in COMP8802 Fundamentals of Computational Intelligence GE. Basic knowledge of programming in an imperative and object-oriented language such as Java and C/C++.