This module will introduce concepts from statistical machine learning (ML), with the aim to provide an in-depth understanding of classical supervised and unsupervised learning models. It will cover analysis of algorithms, implementation, and optimization. This will allow students to start applying ML techniques to solve practical problems.
The module will cover topics such as:
Clustering methods
Classification methods
Introduction to Neural Networks (also touch on Deep Learning)
Other approaches from Statistical Machine Learning.
This module contains a number of learning settings such as lectures, illustrative practical sessions using computers.
Learning Outcomes
By the end of the module students should be able to:
Demonstrate a good understanding of complexity of different statistical machine learning approaches
Demonstrate basic understanding of how to implement ML algorithms, including approaches needed to train machine learning models
Demonstrate conceptual understanding of Computing, Algorithmic and Programming that enables student to evaluate methodologies and develop critiques of them and, where appropriate, propose new methods
Demonstrate an in-depth understanding and ability to perform data analysis using appropriate ML models
Demonstrate self-direction and originality in tackling and solving problems to perform the proper Modelling and optimization
Assessment
Assessment Methods & Exceptions
Assessment:
100% An examination (1.5 hours) consisting of multiple-choice questions and structured answer questions
