| Unit name | Introduction to Machine Learning |
|---|---|
| Unit code | COMS30301 |
| Credit points | 10 |
| Level of study | H/6 |
| 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 | Department of Computer Science |
| Faculty | Faculty of Engineering |
This unit introduces the field of Machine Learning, and teaches how to create and use software that improves with experience. The syllabus of the unit includes: Introduction: tasks, models and features. Binary classification and related tasks. Beyond binary classification. Tree models. Rule models. Linear models. Distance-based models. Probabilistic models. Model ensembles. Machine learning experiments.
After successfully completing this unit, you will be able to: Choose an appropriate learning algorithm for a given problem; Use machine learning algorithms in solving classification problems ; Understand theoretical limitations of machine learning.
20 lectures; problem classes; unsupervised lab sessions.
50% Exam, 50% Coursework
Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Peter Flach. Cambridge University Press. September 2012.
