Module Title:Artificial Intelligence and Machine Learning
Language of Instruction:English
Credits: 5
NFQ Level:8
Module Delivered In 2 programme(s)
Teaching & Learning Strategies: This module will be delivered through a mix of lectures, laboratory assignments, and projects including a professional write-up. It will employ a mixture of active/task-based learning, reflective learning, and problem-based learning.
Module Aim: AI and ML techniques are not new, however, due to the internet's ubiquitous availability of data and compute to train ML networks, their performance has, for example, surpassed that of human visual recognition. This module investigates methods of design, training, and validation of classification neural network models to provide the student with a demonstratable understanding of machine learning's underlying scientific principles.
Learning Outcomes
On successful completion of this module the learner should be able to:
LO1 Demonstrate the differences between artificial intelligence, machine learning and deep learning systems.
LO2 Compose, assemble, clean, and pre-process training data.
LO3 Train image recognition deep learning models.
LO4 Develop and solve computer vision problems with appropriate models.
LO5 Design the components of an image acquisition system.
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.
A high-level language, statistics, linear algebra.

Module Content & Assessment

Indicative Content
Artificial Intelligence
Define AI from narrow to broad to general to super-general artificial intelligence. Give examples of different types and fields of AI, such as text and speech recognition, natural language processing, search and recommendation algorithms, vision detection and recognition.
Neural Networks
Fully connected networks, representation learning models, and convolution neural networks. Different machine learning (ML) models such as LeNet, AlexNet, VGG, Inception, ResNet, Xception, U-net, Fully Convolutional, Attention.
Machine Learning
Supervised, unsupervised, semi-supervised learning, reinforcement learning, and their applications. Linear regression, logistic regression, Support Vector Machines, natural language processing.
Data cleaning and pre-processing
Training data analysis and modelling.
Different training techniques for models, e.g. optimisation, regularisation, batch normalisation, and dropout
Confusion matrices, area under the curve (AUC), receiver operator characteristics (ROC), classification accuracy.
Data privacy, algorithm and data bias, model misuse.
Assessment Breakdown%
Continuous Assessment20.00%
End of Module Formal Examination60.00%
Continuous Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Short Answer Questions n/a 1,2,3 20.00 Week 4
No Project
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Practical/Skills Evaluation n/a 1,2,3,4,5 20.00 Every Week
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam n/a 1,2,3,4,5 60.00 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 Every Week 3.00
Laboratory Every Week 2.00
Independent Learning Every Week 6.00
Total Hours 11.00

Module Delivered In

Programme Code Programme Semester Delivery
CW_EESYS_B Bachelor of Engineering (Honours) in Electronic Engineering 7 Mandatory
CW_EERAS_B Bachelor of Engineering (Honours) in Robotics and Automated Systems 7 Mandatory