Machine learning is not only central to modern artificial intelligence, but across sciences and everyday life. You will learn how computers can program themselves automatically, learning from data or from active exploration of the world. You will develop ability to make sound, rational and effective judgements and decisions about the use of the core machine learning algorithms for different types of practical problems. Selected algorithms will be investigated in great depth, to give you deep understanding of the inner workings of machine learning.
Total Contact time (Lectures) 32
Mini project implementation of a machine learning method worth 60%.
Online Test worth 40%.
Reassessment Method: Like-for-like Including composite form of reassessment for failed practical components – mini-project implementation.
On successfully completing the module, students will be able to:
Appraise and analyse several types of machine learning tasks and applications.
Critically evaluate the strengths and weaknesses of several types of machine learning algorithms.
Appraise and analyse the process of learning from data, which includes the need for generalisation, inductive bias, and the ensuing trade-offs with their implications for practical applications.
Appraise the limits and potential of machine learning and artificial intelligence.
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