Module Details
Module Code: |
LEAD |
Module Title:
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Data Science and Machine Learning 2
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Title:
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Data Science and Machine Learning 2
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Module Level:: |
8 |
Module Coordinator: |
Nigel Whyte
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Module Author:: |
Greg Doyle
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Module Description: |
The aim of this module is to provide students with a comprehensive understanding of and ability to evaluate and utilise data science, AI and machine learning tools and techniques ethically and legally in organisations from a software engineering perspective.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Understand, evaluate, communicate and apply key principles, theories and techniques with respect to data science and machine learning in organisations from a software engineering perspective. |
LO2 |
Understand, evaluate, communicate and apply key principles, theories and techniques (particularly software engineering technologies) with respect to advanced machine learning and deep learning in organisations from a software engineering perspective. |
LO3 |
Understand, evaluate and communicate the key principles, theories and techniques behind ethics, data and legal standards as they relate to data science, machine learnig and deep learning from a software engineering perspective. |
Dependencies |
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 |
Co-requisite Modules
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No Co-requisite modules listed |
Additional Requisite Information
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No Co Requisites listed
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Indicative Content |
AI, ML, D
1. What are artificial intelligence (AI), machine learning (ML), deep learning (DL)
2. Representations and software tools, techniques and technologies and representations used in machine learning and deep learning
3. Introduction to supervised, unsupervised, semi-supervised, reinforcement learning etc.
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Unsupervised Machine Learning
1, Unsupervised machine learning including clustering - k-means, k-medoids, fuzzy c-means, agglomerative and divisive hierarchical clustering etc.
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Supervised Machine Learning
1. Supervised machine learning to include, for example, support vector machines, naïve Bayesian classifiers, k-nearest neighbour and introduction to neural networks etc.
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Deep Learning
1. Neural networks - back-propagation, gradient descent standard feed forward neural networks, recurrent neural networks, convolutional neural networks etc.
2. Deep learning application areas - natural language processing, image processing, spam detection etc.
3. Deep learning architectures etc.
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Reinforcement Learning and Emerging AI/ML techniques
1. Reinforcement Learning, Q learning, State–action–reward–state–action (SARSA), Monte Carlo methods, DQN etc
2. Emerging machine learning, deep learning tools and techniques
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Module Content & Assessment
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Assessment Breakdown | % |
Project | 60.00% |
End of Module Formal Examination | 40.00% |
AssessmentsFull Time
End of Module Formal Examination |
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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.
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Reassessment Description Typically, learners are reassessed via a repeat of the End of Module Formal Examination
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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 |
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Contact |
Lecture |
12 Weeks per Stage |
2.00 |
24 |
Estimated Learner Hours |
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Non Contact |
Estimate Learner Hours |
15 Weeks per Stage |
5.13 |
77 |
Laboratory |
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Contact |
Laboratory |
12 Weeks per Stage |
2.00 |
24 |
Total Weekly Contact Hours |
4.00 |
Module Resources
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Recommended Book Resources |
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Burkov, A.. (2019), The Hundred-Page Machine Learning Book, [ISBN: 978199957950].
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Ng, A.,. (2017), Machine Learning Yearning.
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Ian Goodfellow,Yoshua Bengio,Aaron Courville. (2016), Deep Learning, MIT Press, p.775, [ISBN: 9780262035613].
| Supplementary Book Resources |
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Geron, A.. (2019), Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems, Second. [ISBN: 978149196229].
| This module does not have any article/paper resources |
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This module does not have any other resources |
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