Module Details

Module Code: TECH
Module Title: Artificial Intelligence in the Wild
Title: Artificial Intelligence in the Wild
Module Level:: 8
Credits:: 5
Module Coordinator: Nigel Whyte
Module Author:: Oisin Cawley
Domains:  
Module Description: 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:
# 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 Assessment30.00%
Project30.00%
End of Module Formal Examination40.00%

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
This module does not have any other resources
Discussion Note: