MA5832 - Data Mining and Machine Learning

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

• Written > Problem task - (60%) - Individual
• Written > Project report - (40%) - Individual.

Note: 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.