Module Details
Module Code: |
GAME |
Module Title:
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Machine Learning for Games
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Title:
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Machine Learning for Games
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Module Level:: |
8 |
Module Coordinator: |
Nigel Whyte
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Module Author:: |
Oisin Cawley
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Module Description: |
To immerse students in the formal theory, and the application of contemporary techniques in Machine Learning for computer games development.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Demonstrate an excellent understanding of non symbolic approaches to Artificial Intelligence |
LO2 |
Understand, evaluate and communicate the key principles, theories and techniques specific to the training of Machine Learning models. |
LO3 |
Apply key principles, theories and techniques (particularly Machine Learning technologies) with respect to computer games development. |
Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Additional Requisite Information
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No Co Requisites listed
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Indicative Content |
Introduction to Machine Learning
Probability, Inference, Clustering, N-Gram Prediction
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Artificial Neural Networks
Perceptron, Multilayer Networks, Backpropagation, Simmulated Annealing
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Genetic Algorithms
Genetic encoding, Genetic Operators, Selection, Mutation, Combining GAs and Neural Networks
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Agent Based Systems and Reinforcement Learning
ABS concepts, Reinforcement Learning, q-Learning, DQN
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Module Content & Assessment
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Assessment Breakdown | % |
Continuous Assessment | 30.00% |
Project | 20.00% |
End of Module Formal Examination | 50.00% |
AssessmentsFull Time
End of Module Formal Examination |
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Reassessment Requirement |
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
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SETU Carlow Campus reserves the right to alter the nature and timings of assessment
Module Workload
Workload: Full Time |
Workload Type |
Workload Category |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
Lecture |
|
Contact |
2 Lectures per week |
12 Weeks per Stage |
2.00 |
24 |
Laboratory |
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Contact |
2 sessions per week |
12 Weeks per Stage |
2.00 |
24 |
Estimated Learner Hours |
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Non Contact |
Estimated Learner Hours |
15 Weeks per Stage |
5.13 |
77 |
Total Weekly Contact Hours |
4.00 |
Module Resources
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Recommended Book Resources |
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Stuart J. Russell and Peter Norvig; contributing writers, John F. Canny... [et al.]. (2003), Artificial intelligence, Prentice Hall/Pearson Education, Upper Saddle River, N.J., [ISBN: 978-0130803023].
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Richard Sutton and Andrew Barto. (2020), Reinforcement Learning, 2nd.
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Michael Wooldridge. (2009), An introduction to multiagent systems, John Wiley & Sons, Chichester, U.K., [ISBN: 0470519460].
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Ian Millington. Artificial Intelligence for games, Morgan Kaufman, [ISBN: 978-012374731].
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Kevin Gurney. (1997), An introduction to neural networks, UCL Press, London, [ISBN: 978-1857285031].
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Mat Buckland. Programming Game AI By Example, Wordware Publishing, p.495, [ISBN: 9781556220784].
| This module does not have any article/paper resources |
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This module does not have any other resources |
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