Module Details

Module Code: LEAD
Module Title: Data Science and Machine Learning 2
Title: Data Science and Machine Learning 2
Module Level:: 8
Credits:: 5
Module Coordinator: Nigel Whyte
Module Author:: Greg Doyle
Domains:  
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.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# 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.
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed
 
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.
Unsupervised Machine Learning
1, Unsupervised machine learning including clustering - k-means, k-medoids, fuzzy c-means, agglomerative and divisive hierarchical clustering etc.
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.
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.
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
Module Content & Assessment
Assessment Breakdown%
Project60.00%
End of Module Formal Examination40.00%

Assessments

Full Time

No Continuous Assessment
Project
Assessment Type Project % of Total Mark 60
Timing Week 12 Learning Outcomes 1,2,3
Non-marked No
Assessment Description
Practical programming project - the purpose of this applied project is to allow the learner, for example, to follow the data science process and prepare data so that statistical/ML techniques can be applied to the data to gain insights. This project may/may not have a significant group aspect at the discretion of the module lecturer and will typically involve a significant applied/programming component
No Practical
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 40
Timing End-of-Semester Learning Outcomes 1,2,3
Non-marked No
Assessment Description
Final written en of module examination
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.
Reassessment Description
Typically, learners are reassessed via a repeat of the End of Module Formal Examination

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 Lecture 12 Weeks per Stage 2.00 24
Estimated Learner Hours Non Contact Estimate Learner Hours 15 Weeks per Stage 5.13 77
Laboratory Contact Laboratory 12 Weeks per Stage 2.00 24
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Burkov, A.. (2019), The Hundred-Page Machine Learning Book, [ISBN: 978199957950].
  • Ng, A.,. (2017), Machine Learning Yearning.
  • Ian Goodfellow,Yoshua Bengio,Aaron Courville. (2016), Deep Learning, MIT Press, p.775, [ISBN: 9780262035613].
Supplementary Book Resources
  • 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
This module does not have any other resources
Discussion Note: