Module Title:Machine Learning for Games
Language of Instruction:English
Credits: 5
NFQ Level:8
Module Delivered In 1 programme(s)
Teaching & Learning Strategies: Traditional lectures are used to convey knowledge from teacher to student, and students are actively encouraged to engage in discussion during class. During the practical sessions, students will undertake various laboratory exercises implementing and exploring a variety of algorithms. Group learning is also utilised via a module group project and also a cross-module group project as possible. A term paper will involve a more in-depth study of the topics raised.
Module Aim: 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 should be able to:
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.
Pre-requisite learning
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.

No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed.
No requirements listed
 

Module Content & Assessment

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
Assessment Breakdown%
Continuous Assessment30.00%
Project20.00%
End of Module Formal Examination50.00%
Continuous Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Case Studies Students are required to implement specific algorithms within a gaming context 1,2,3 30.00 n/a
Project
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project Intended as a cross-module project 2,3 20.00 n/a
No Practical
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam A written assessment of student's understanding and ability to conceptually apply the course material appropriately. 1,2,3 50.00 End-of-Semester

ITCarlow reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Frequency Average Weekly Learner Workload
Lecture 12 Weeks per Stage 2.00
Laboratory 12 Weeks per Stage 2.00
Estimated Learner Hours 15 Weeks per Stage 5.13
Total Hours 125.00
 

Module Delivered In

Programme Code Programme Semester Delivery
CW_KCCGD_B Bachelor of Science (Honours) in Computer Games Development 8 Group Elective 1