Course Details in

Module Title	LH Machine Learning
School	Computer Science
Department	Computer Science
Module Code	06 26428
Module Lead	Dr Ata Kaban
Level	Honours Level
Credits	10
Semester	Semester 1
Pre-requisites	Introduction to AI - (06 23069)
Co-requisites
Restrictions	Prohibited Module Combinations: 020640 Machine Learning, 20236 Machine Learning (Extended)
Contact Hours	Lecture-23 hours Guided independent study-77 hours Total: 100 hours
Exclusions
Description	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. The aims of this module are to: a) Introduce the basic concepts and terminology of machine learning b) Give an overview of the main approaches to machine learning. c) Show similarities and differences between different approaches. d) Present basic principles for the classification of approaches to machine learning. e) Give practical experience of applying machine learning algorithms to classification and data analysis problems.
Learning Outcomes	By the end of the module students should be able to: Demonstrate a knowledge and understanding of the main approaches to machine learning. Demonstrate the ability to apply the main approaches to unseen examples. Demonstrate an understanding of the differences, advantages and problems of the main approaches in machine learning. Demonstrate an understanding of the main limitations of current approaches to machine learning, and be able to discuss possible extensions to overcome these limitations. Demonstrate a practical understanding of the use of machine learning algorithms.
Assessment	26428-01 : Examination : Exam (Centrally Timetabled) - Written Unseen (80%) 26428-02 : Continuous Assessment : Coursework (20%)
Assessment Methods & Exceptions	1.5 hr examination (80%), continuous assessment (20%)
Other	None
Reading List