Module 26428 (2019)
Module description - Machine Learning
The Module Description is a strict subset of the Syllabus Page.
|Module Title||Machine Learning|
|School||School of Computer Science|
|Member of Staff||Iain Styles|
|Semester||Semester 1 - 10 credits|
2 hrs lectures, exercise and laboratory classes per week.
The module will provide a solid foundation to machine learning. It will give an overview of many of the core concepts, methods, and algorithms in machine learning, covering several forms of supervised and unsupervised learning. It also introduces the basics of computational learning theory, leading up to more advanced topics like boosting and ensemble methods. The module will give the student a good understanding of how, why and when do various modern machine learning methods work. It will also give them experience of applying machine learning methods in practice, and an awareness of the issues, techniques and open problems posed by high dimensionality in machine learning.
On successful completion of this module, the student should be able to:
Sessional: 1.5 hr examination (80%), continuous assessment (20%).
Supplementary (where allowed): By examination only.