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COM3023 - Machine Learning and AI (2023)
MODULE TITLE | Machine Learning and AI | CREDIT VALUE | 15 |
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MODULE CODE | COM3023 | MODULE CONVENER | Dr Tinkle Chugh (Coordinator) |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 0 | 12 weeks | 0 |
Number of Students Taking Module (anticipated) | 30 |
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This module will explore machine learning and artificial intelligence at an advanced level. It examines some of the theoretical foundations of machine learning and AI, together with some advanced techniques. In particular it examines Bayesian probability and fuzzy logic for dealing with uncertainty, and their relations to information theory. It also introduces techniques for dealing with temporally or spatially structured data, and reinforcement learning.
The aim of this module is to provide a strong theoretical basis for machine learning methods that you have already encountered and to introduce new methods for connected data and reinforcement learning. It aims to build on and enhance your analytical skills, and to put into practice methods for new machine learning and AI paradigms.
On successful completion of this module you should be able to:
Module Specific Skills and Knowledge
4. Understand principles of machine learning and AI for spatially and temporally connected models;
Discipline Specific Skills and Knowledge
7. Learn a variety machine learning and AI methods and apply them to real problems.
Personal and Key Transferable / Employment Skills and Knowledge
9. Adapt existing technical knowledge to learning new methods.
Indicative syllabus plan; precise content may vary from year to year.
- Bayesian methods: theoretical perspectives; conjugate families; Monte Carlo sampling methods; approximations including Laplace approximations, variational approximation, expectation propagation.
- Fuzzy logic: measurements and modelling in the face of incomplete knowledge; vagueness and uncertainty. Fuzzy set theory; Dempster-Shafer theory; fuzzy logic operators and process.
- Information theory: information, entropy; coding; learning from an information theoretic point of view.
- Learning in spatially and temporally connected models: Hidden Markov models; Markov Random Fields.
- Reinforcement learning: Multi-armed bandits; finite Markov decision processes; temporal difference learning; on and off policy learning.
Scheduled Learning & Teaching Activities | 35.00 | Guided Independent Study | 115.00 | Placement / Study Abroad | 0.00 |
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Category | Hours of study time | Description |
Scheduled Learning and Teaching | 20 | Lectures |
Scheduled Learning and Teaching | 15 | Workshops and tutorials |
Guided Independent Study | 115 | Coursework, private study, reading |
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Coursework | 30 | Written Exams | 70 | Practical Exams | 0 |
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Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Written exam | 70 | 2 hours | 1, 2, 3, 4, 5, 6 | Orally on request |
Technical exercise and report | 30 | 30 hours | 1, 2, 3, 4, 5, 6, 7, 8, 9 | Written |
Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
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Written Exam | Written exam (2 hours) | 1-6 | August Ref/Def period |
Technical Exercise and Report 1 | Technical Exercise and Report | All | August Ref/Def period |
Reassessment will be by coursework and/or written exam in the failed or deferred element only. For referred candidates, the module mark will be capped at 40%. For deferred candidates, the module mark will be uncapped.
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
ELE: http://vle.exeter.ac.uk/
Web based and Electronic Resources:
Other Resources:
Reading list for this module:
Type | Author | Title | Edition | Publisher | Year | ISBN | Search |
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Set | Bishop, C. | Pattern Recognition and Machine Learning | 1 | Springer | 2006 | 978-0387310732 | [Library] |
Set | Russell, S. and Norvig, P. | Artificial Intelligence: A Modern Approach | 4 | Pearson | 2016 | 978-1292153964 | [Library] |
Set | Mackay, D.J.C. | Information Theory, Inference, and Learning | 1 | Cambridge | 2006 | 978-0521642989 | [Library] |
Set | Hastie, T., Tibshirani, R., and Friedman, J. | The Elements of Statistical Learning: Data Mining, Inference, and Prediction | 2 | Springer | 2017 | 978-0387848570 | [Library] |
Set | Sutton, R.S., Barto, A. and Bach, F. | Reinforcement Learning: An Introduction | 2 | MIT Press | 2018 | 978-0262039246 | [Library] |
CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
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PRE-REQUISITE MODULES | MTH2006, COM2011 |
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CO-REQUISITE MODULES |
NQF LEVEL (FHEQ) | 6 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Friday 12 April 2019 | LAST REVISION DATE | Tuesday 24 January 2023 |
KEY WORDS SEARCH | None Defined |
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