Module Details

Module Code: GAME
Module Title: Machine Learning for Games
Title: Machine Learning for Games
Module Level:: 8
Credits:: 5
Module Coordinator: Nigel Whyte
Module Author:: Oisin Cawley
Domains:  
Module Description: To immerse students in the formal theory, and the application of contemporary techniques in Machine Learning for computer games development.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# 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.
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed
 
Indicative Content
Introduction to Machine Learning
Probability, Inference, Clustering, N-Gram Prediction
Artificial Neural Networks
Perceptron, Multilayer Networks, Backpropagation, Simmulated Annealing
Genetic Algorithms
Genetic encoding, Genetic Operators, Selection, Mutation, Combining GAs and Neural Networks
Agent Based Systems and Reinforcement Learning
ABS concepts, Reinforcement Learning, q-Learning, DQN
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment30.00%
Project20.00%
End of Module Formal Examination50.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Case Studies % of Total Mark 30
Timing n/a Learning Outcomes 1,2,3
Non-marked No
Assessment Description
Students are required to implement specific algorithms within a gaming context
Project
Assessment Type Project % of Total Mark 20
Timing n/a Learning Outcomes 2,3
Non-marked No
Assessment Description
Intended as a cross-module project
No Practical
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 50
Timing End-of-Semester Learning Outcomes 1,2,3
Non-marked No
Assessment Description
A written assessment of student's understanding and ability to conceptually apply the course material appropriately.
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.

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 Contact 2 sessions per week 12 Weeks per Stage 2.00 24
Estimated Learner Hours Non Contact Estimated Learner Hours 15 Weeks per Stage 5.13 77
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • 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].
  • Richard Sutton and Andrew Barto. (2020), Reinforcement Learning, 2nd.
  • Michael Wooldridge. (2009), An introduction to multiagent systems, John Wiley & Sons, Chichester, U.K., [ISBN: 0470519460].
  • Ian Millington. Artificial Intelligence for games, Morgan Kaufman, [ISBN: 978-012374731].
  • Kevin Gurney. (1997), An introduction to neural networks, UCL Press, London, [ISBN: 978-1857285031].
  • Mat Buckland. Programming Game AI By Example, Wordware Publishing, p.495, [ISBN: 9781556220784].
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: