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 will be able to:
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Learning Outcome Description
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.
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 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).
Module Content & Assessment
Assessment Breakdown
%
Continuous Assessment
30.00%
Project
30.00%
End of Module Formal Examination
40.00%
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 A number of lab based exercises.
Project
Assessment Type
Project
% of Total Mark
30
Timing
n/a
Learning Outcomes
1,2,3
Non-marked
No
Assessment Description Individual/Group Projects
No Practical
End of Module Formal Examination
Assessment Type
Formal Exam
% of Total Mark
40
Timing
End-of-Semester
Learning Outcomes
1,2
Non-marked
No
Assessment Description Written examination of module content.
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
1 hour per week
12 Weeks per Stage
1.00
12
Laboratory
Contact
3 hours per week
12 Weeks per Stage
3.00
36
Independent Learning Time
Non Contact
No Description
15 Weeks per Stage
5.13
77
Total Weekly Contact Hours
4.00
Module Resources
This module does not have any book resources
This module does not have any article/paper resources