Module Details

Module Code: ELEC C4602
Module Title: Artificial Intelligence and Machine Learning
Title: Artificial Intelligence and Machine Learning
Module Level:: 8
Credits:: 5
Module Coordinator: Frances Hardiman
Module Author:: James Garland
Domains:  
Module Description: 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 will be able to:
# Learning Outcome Description
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.
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
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.
Training
Different training techniques for models, e.g. optimisation, regularisation, batch normalisation, and dropout
Metrics
Confusion matrices, area under the curve (AUC), receiver operator characteristics (ROC), classification accuracy.
Ethics
Data privacy, algorithm and data bias, model misuse.
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment20.00%
Practical20.00%
End of Module Formal Examination60.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Short Answer Questions % of Total Mark 20
Timing Week 4 Learning Outcomes 1,2,3
Non-marked No
Assessment Description
n/a
No Project
Practical
Assessment Type Practical/Skills Evaluation % of Total Mark 20
Timing Every Week Learning Outcomes 1,2,3,4,5
Non-marked No
Assessment Description
n/a
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 60
Timing End-of-Semester Learning Outcomes 1,2,3,4,5
Non-marked No
Assessment Description
n/a
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 No Description Every Week 3.00 3
Laboratory Contact No Description Every Week 2.00 2
Independent Learning Non Contact No Description Every Week 6.00 6
Total Weekly Contact Hours 5.00
 
Module Resources
Recommended Book Resources
  • Géron, A.. (2019), Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems., O'Reilly Media.
  • Goodfellow, I., Bengio, Y., Courville, A.. (2016), Deep learning, 2. Cambridge: MIT press.
  • Bishop, C. (2006), Pattern Recognition and Machine Learning., Springer.
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: