MSc ACS: Computer Security
Year of entry: 2020
Course unit details:
Foundations of Machine Learning
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 1|
|Offered by||Department of Computer Science|
|Available as a free choice unit?||Yes|
- To introduce the main algorithms used in modern machine learning.
- To introduce the theoretical foundations of machine learning.
- To provide practical experience of applying machine learning techniques.
If you have sat an undergraduate ML course (particularly my COMP24111) then you may feel you know all this material. In fact we will cover virtually the same topics - however, you almost certainly will not have covered this material in the same depth as we will cover it. We will study why and how these methods work, at a very deep level. This is not a course on how to use ML techniques. It is a course on the foundations, the deeper aspects. If you really think you know it all already, then try sitting the previous exam papers, under exam conditions of course (i.e. no textbooks).
Have knowledge and understanding of the principle algorithms used in modern machine learning, as outlined in the syllabus.
Have sufficient knowledge of information theory and probability theory to understand some basic theoretical results in machine learning.
Be able to apply machine learning algorithm to real datasets, evaluate their performance and appreciate the practical issues involved.
Be able to provide a clear and concise description and justification for the employed experimental procedures.
- Classifiers and the Nearest Neighbour Rule
- Linear Models, Support Vector Machines
- Algorithm assessment - overfitting, generalisation, comparing two algorithms
- Decision Trees, Feature Selection, Mutual Information
- Probabilistic Classifiers and Bayes Theorem
- Combining Models - ensemble methods, mixtures of experts, boosting
- Feature Selection - basic methods, plus some tasters of research material
- Write a 6 page research paper applying appropriate ML techniques on supplied datasets.
Teaching and learning methods
1 day per week (5 weeks)
- Analytical skills
- Project management
- Oral communication
- Problem solving
- Written communication
|Written assignment (inc essay)||50%|
COMP61011 reading list can be found on the School of Computer Science website for current students.
|Scheduled activity hours|
|Assessment written exam||2|
|Practical classes & workshops||20|
|Independent study hours|
|Gavin Brown||Unit coordinator|
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.