
Course unit details:
Foundations of Machine Learning
Unit code | COMP61011 |
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Credit rating | 15 |
Unit level | FHEQ level 7 – master's degree or fourth year of an integrated master's degree |
Teaching period(s) | Semester 1 |
Available as a free choice unit? | Yes |
Overview
Aims
- 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).
Learning outcomes
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Have knowledge and understanding of the principle algorithms used in modern machine learning, as outlined in the syllabus.
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Have sufficient knowledge of information theory and probability theory to understand some basic theoretical results in machine learning.
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Be able to apply machine learning algorithm to real datasets, evaluate their performance and appreciate the practical issues involved.
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Be able to provide a clear and concise description and justification for the employed experimental procedures.
Syllabus
Topics covered:
- 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
Teaching and learning methods
Lectures
1 day per week (5 weeks)
Employability skills
- Analytical skills
- Innovation/creativity
- Project management
- Oral communication
- Problem solving
- Research
- Written communication
Assessment methods
Method | Weight |
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Written exam | 80% |
Written assignment (inc essay) | 20% |
Feedback methods
- Formative exam practice questions with answers and discussion
- Virtual face-to-face feedback on lab work
Study hours
Scheduled activity hours | |
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Assessment written exam | 2 |
Lectures | 10 |
Practical classes & workshops | 20 |
Independent study hours | |
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Independent study | 118 |
Teaching staff
Staff member | Role |
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David Wong | Unit coordinator |