Unit information: Introduction to Machine Learning in 2016/17

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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

Description including Unit Aims

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.

Intended Learning Outcomes

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.

Teaching Information

20 lectures; problem classes; unsupervised lab sessions.

Assessment Information

50% Exam, 50% Coursework

Reading and References

Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Peter Flach. Cambridge University Press. September 2012.

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