Module Details

Module Code: INTR8041
Title: Cloud Based Machine Learning
Long Title: Cloud Based Machine Learning
NFQ Level: Advanced
Valid From: Semester 2 - 2021/22 ( January 2022 )
Duration: 1 Semester
Credits: 5
Field of Study: 5213 - Interdisciplinary Engineering
Module Delivered in: 1 programme(s)
Module Description: This module introduces the student to the fundamental concepts of both Machine Learning and Data Analytics in order to provide students with cloud based methods and procedures to develop new insights into the vast array of data now available in today's digital systems.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Demonstrate an understanding of a range of machine learning approaches.
LO2 Demonstrate detailed knowledge of the use of cloud based machine learning systems for data processing and analytics.
LO3 Design and evaluate the performances of various machine learning methods for data analytics.
LO4 Apply cloud based machine learning systems to solve real-world problems.
LO5 Think critically about machine learning model results.
LO6 Demonstrate ethical thinking in data processing and machine learning.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Machine learning principles and applications
Overview of common machine learning techniques (e.g. decision trees, Bayesian networks, instance-based learning, reinforcement learning, artificial neural networks, clustering, K-means, self-organizing map).
Visualizing and preparation of a dataset for training
Choice of features, including for time series, images, text and dimension reduction.
Model selection
Train and evaluate machine learning models (e.g. linear, kernel, neural net).
Building, training, and deploying machine learning models in the cloud
Models building, training, tuning, and deployment with cloud based tools such as Amazon SageMaker.
Practical experience of applying machine learning methods to real data
Create of machine learning systems for a variety of real-life applications, using supervised or unsupervised learning.
Ethics
Consideration of the ethical issues raised by data processing and machine learning and the power of inference in particular beyond the personal data.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Practical/Skills Evaluation % of Total Mark 50
Timing Every Second Week Learning Outcomes 1,2,3,4,5,6
Assessment Description
Series of laboratories to explore the topics and provide concrete examples, with relevant technical exercises to each laboratory requiring handup. E.g. laboratories on (a) data pre-processing and analytics (b) machine learning methods (c) designing and applying cloud based machine learning systems.
Assessment Type Project % of Total Mark 50
Timing Week 13 Learning Outcomes 1,2,3,4,5,6
Assessment Description
Project to apply and implement elements of coursework. Project may be individual or team based, e.g. (a) create requirements for a specific real-life problem, (b) develop a machine learning-based solution (c) implement and test on a cloud based system (d) analysing machine learning model results and (e) write a report with consideration and appreciation of machine learning ethics.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lab Contact Machine learning development while observing industry best practice and data ethics Every Week 4.00 4
Independent & Directed Learning (Non-contact) Non Contact Review of lecture notes, resources, preparation for assessment deliverables. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lab Contact Machine Learning Development while observing industry best practice and data ethics Every Week 3.00 3
Independent & Directed Learning (Non-contact) Non Contact Review of lecture notes, resources, preparation for assessment deliverables. Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Oliver Theobald. (2017), Machine Learning For Absolute Beginners: A Plain English Introduction, 2nd. Scatterplot Press, p.164, [ISBN: B07335JNW1].
Supplementary Book Resources
  • Stephen Marsland. (2014), Machine Learning: An Algorithmic Perspective, 2nd. Chapman and Hall, [ISBN: 1466583282].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_ESMPR_8 Bachelor of Engineering (Honours) in Smart Product Engineering 8 Mandatory