Module Title:Artificial Intelligence in the Wild
Language of Instruction:English
Credits: 5
NFQ Level:8
Module Delivered In 3 programme(s)
Teaching & Learning Strategies: As well as traditional lectures, students will undertake various laboratory exercises implementing a number of algorithms/techniques. They will be expected to participate in class on the materials covered, in addition to both individual and group based projects.
Module Aim: The aim is for students to understand the formal theory, current technologies and techniques for the application of Artificial Intelligence in real world contexts. The module will focus on students applying their new knowledge by practical applications in both virtual and physical devices.
Learning Outcomes
On successful completion of this module the learner should be able to:
LO1 Understand, evaluate and communicate the key principles, theories and techniques specific to the application of Artificial Intelligence.
LO2 Understand and critique the application of Artificial Intelligence/Machine Learning approaches in practice.
LO3 Design, implement and test appropriate Artificial Intelligence algorithms and prototypes for varied problem domains and contexts.
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
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 Artificial Intelligence
A brief history of AI. Disambiguation between terms such as Artificial Intelligence, Machine Learning, Deep Learning and Data Science.
Machine learning
Machine learning and knowledge acquisition to include basic concepts such as search techniques, distance measures, linear models, K nearest neighbours.
Evolving Intelligence
Focusing on non-symbolic AI such as Neural Networks and Genetic Algorithms.
Programming AI
A selection of current technologies/software applications such as Python, Tensorflow, sklearn.
AI applications in the real world
Learning how to develop solutions within real time and physical contexts such as Object Detection, Image recognition, Robotics, and Natural Language Processing.
Intelligence at the Edge
Understanding the constraints/requirements for power, memory, and storage when dealing with stand alone systems in the field (edge computing).
Assessment Breakdown%
Continuous Assessment30.00%
End of Module Formal Examination40.00%
Continuous Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Case Studies A number of lab based exercises. 1,2,3 30.00 n/a
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project Individual/Group Projects 1,2,3 30.00 n/a
No Practical
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam Written examination of module content. 1,2 40.00 End-of-Semester

SETU Carlow Campus 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 1.00
Laboratory 12 Weeks per Stage 3.00
Independent Learning Time 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
CW_KCCYB_B Bachelor of Science (Honours) in Cyber Crime and IT Security 8 Elective
CW_KCSOF_B Bachelor of Science (Honours) in Software Development 8 Group Elective 1