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 |
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.
After successfully completing this unit, you will be able to
Teaching will be delivered through a series of mostly synchronous sessions, including lectures, seminars, practical activities, discussion groups and self-directed exercises.
1 Summative Assessment, 100% - Coursework. This will assess all ILOs.
Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Peter Flach. Cambridge University Press. September 2012.