Module Title: | Artificial Intelligence and Machine Learning |
Language of Instruction: | English |
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.
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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
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No Co-requisite modules listed |
Requirements
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.
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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.
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Machine Learning
Supervised, unsupervised, semi-supervised learning, reinforcement learning, and their applications. Linear regression, logistic regression, Support Vector Machines, natural language processing.
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Data cleaning and pre-processing
Training data analysis and modelling.
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Training
Different training techniques for models, e.g. optimisation, regularisation, batch normalisation, and dropout
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Metrics
Confusion matrices, area under the curve (AUC), receiver operator characteristics (ROC), classification accuracy.
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Ethics
Data privacy, algorithm and data bias, model misuse.
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Assessment Breakdown | % |
Continuous Assessment | 20.00% |
Practical | 20.00% |
End of Module Formal Examination | 60.00% |
Continuous Assessment |
Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
Short Answer Questions |
n/a |
1,2,3 |
20.00 |
Week 4 |
Practical |
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 |
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 |
Every Week |
3.00 |
Laboratory |
Every Week |
2.00 |
Independent Learning |
Every Week |
6.00 |
Total Hours |
11.00 |
Module Delivered In
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