James Cook University Subject Handbook - 2020

MA5832 - Data Mining and Machine Learning

Subject Description

This subject will provide students with a range of algorithms based on machine learning techniques for advanced data analysis and mining. These algorithms and techniques fall within the most common machine learning paradigms, namely, unsupervised, semi-supervised, and supervised learning. In particular, students will learn sophisticate machine learning methods for clustering, outlier detection, classification, feature selection, and regression.

Learning Outcomes

• explain what machine learning for data mining is about and identify the most common tasks and roles of machine learning in the realm of data mining
• describe, choose, and apply unsupervised machine learning methods for descriptive data mining tasks, such as clustering and outlier detection
• describe, choose, and apply supervised techniques for dimensionality reduction via feature selection
• describe, choose, and apply semi-supervised and/or supervised machine learning methods for predictive data mining tasks, such as pattern classification and regression

Subject Assessment

• Invigilated > Quizzes or tests - (20%)
• Non-Invigilated > Assignments - (60%)
• Computational laboratories/log book - (20%)

Note that minor variations might occur due to the continuous subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.

Availabilities