Unit information: Machine Learning Paradigms in 2020/21

Unit name	Machine Learning Paradigms
Unit code	COMSM0025
Credit points	10
Level of study	M/7
Teaching block(s)	Teaching Block 1 (weeks 1 - 12)
Unit director	Professor. Peter Flach
Open unit status	Not open
Pre-requisites	None
Co-requisites	None
School/department	School of Computer Science
Faculty	Faculty of Engineering

Description including Unit Aims

This unit gives an in-depth overview of Machine Learning, exploring both unity and diversity among different ML paradigms and why this diversity is needed and how it can be exploited. The paradigms covered include: Introduction: tasks, models and features; Tree and Rule models; Linear and Distance-based models; Probabilistic models; Model ensembles; Deep learning. The unit will provide students with a solid analytical and practical framework for further work in data-driven AI.

Intended Learning Outcomes

After successfully completing this unit, you will be able to

1. Choose an appropriate learning algorithm for a given problem;
2. Use machine learning algorithms in solving classification problems;
3. Understand theoretical and practical limitations of machine learning.

Teaching Information

Teaching will be delivered through a series of mostly synchronous sessions, including lectures, seminars, practical activities, discussion groups and self-directed exercises.

Assessment Information

1 Summative Assessment, 100% - Coursework. This will assess all ILOs.

Reading and References

Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Peter Flach. Cambridge University Press. September 2012.