| Unit name | Machine Learning |
|---|---|
| Unit code | COMS30007 |
| Credit points | 10 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Ek |
| Open unit status | Not open |
| Pre-requisites |
None |
| Co-requisites |
None |
| School/department | Department of Computer Science |
| Faculty | Faculty of Engineering |
Machine Learning is the science of how we can build abstractions of the world from data. In this unit we will start with the fundamental underlying principles and philosophies that allows us to learn and then look at how we can formulate these using explicit models.
Machine learning is mathematical in nature and a good grasp of linear algebra and multi-variate calculus is required to fully digest the material.
After successfully completing this unit, you will be able to understand the fundamental principles of machine learning and how to build models of data.
18 lectures; 9 lab sessions
100% Exam
Bishop, C. M. (2006). Pattern recognition and machine learning.
