Machine Learning - 2023 entry
MODULE TITLE | Machine Learning | CREDIT VALUE | 15 |
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MODULE CODE | COMM036DA | MODULE CONVENER | Unknown |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 12 |
Number of Students Taking Module (anticipated) |
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The Machine Learning module equips students with the skills to employ machine learning techniques and statistical modelling for data-driven decision-making and solving live commercial problems. Students will conduct high-quality investigations using analytical software, applying key algorithms and models to develop effective analytical solutions. They will learn how machine learning benefits organisations and the principles of data-driven analysis. The module emphasises the selection of relevant data, model fitting, and evaluation for solving complex data problems. By the end of the module, students will possess the necessary expertise to leverage machine learning algorithms, make informed decisions, and derive valuable insights for addressing real-world business challenges.
Pre-requisite modules: None.
Co-requisite modules: None.
This module is a part of MSc Digital and Technology Solutions (Integrated Degree Apprenticeship) programme. It cannot be taken as an elective by students on other programmes.
The apprenticeship standard and other documentation relating to the Level 7 Digital and Technology Solutions (Data Analyst Specialist) Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeship-standards/digital-and-technology-solutions-specialist-integrated-degree/
On the completion of this module, you will be able to leverage machine learning algorithms, discover patterns in your data, make data driven decisions to solve live commercial problems. In particular, this module aims to impart knowledge and understanding of machine learning methods from basic pattern-analysis methods to state-of-the-art technology. This gives you the experience of applying machine learning for data driven analysis, selecting data for training, model fitting, development and evaluation for extracting knowledge and patterns from large datasets. Recent development of techniques and algorithms for big-data analysis will also be addressed. You will learn a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition for making informed decisions, and derive valuable insights for addressing real-world business and industrial challenges.
On successful completion of this module you should be able to:
Module Specific Skills and Knowledge
2. Analyse novel pattern recognition and classification problems, establish statistical models for them and employ analytical software to solve them
Discipline Specific Skills and Knowledge
5. Synthesise a number of complex and advanced mathematical and numerical techniques to a wide range of problems and domains
Personal and Key Transferable / Employment Skills and Knowledge
7. Demonstrate results and outcomes driven to achieve high key performance outcomes for digital and technology solutions objectives
Whilst the module’s precise content may vary from year to year, an example of an overall structure is as follow:
- Practical motivation for machine learning,
- Basic ideas of supervised and unsupervised learning, classification, regression.
- Describing data.
- Latent descriptions: k-means, maximum likelihood; mixture models; PCA; ICA.
- Supervised models: Neural networks and deep learning, maximum margin classifiers.
- Loss functions and maximum likelihood estimators.
- Evaluation of performance, dataset balance.
- Ensemble methods: boosting, bagging, decision trees and random forests Metric learning.
Scheduled Learning & Teaching Activities | 20 | Guided Independent Study | 130 | Placement / Study Abroad | 0 |
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Category | Hours of study time | Description |
Scheduled Learning and Teaching Activity | 20 |
Masterclasses & Webinars |
Scheduled Learning and Teaching Activity | 6 | Asynchronous Online classes |
Guided Independent Study | 124 |
Background reading, practice and preparation for assessments. Application of knowledge in workplace and demonstration of skills. |
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Online tests | 1 hour | 1-5 | Verbal - online |
Coursework | 100 | Written Exams | 0 | Practical Exams | 0 |
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Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Technical Report | 40 | 2000 words | 1-8 | Written feedback from tutor |
Technical Report | 60 | 3000 words | 1-8 | Written feedback from tutor |
Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
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Technical Report (40%) |
Resubmission |
1-8 |
Programme schedule dependent |
Technical Report (60%) |
Resubmission |
1-8 |
Programme schedule dependent |
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
- Bishop, C. (2007 Pattern Recognition and Machine Learning, Springer.
- Webb, A. (2002) Statistical Pattern Recognition, 2nd edition, Wiley
- Shawe-Taylor, J. and Cristianini, N. (2006), Kernel methods for pattern analysis, Cambridge University Press
- Murphy, K. (2012) Machine Learning: A Probabilistic Perspective, MIT Press
- P. Tan, M. Steinbach, V. Kumar(2014) Introduction to Data Mining. Pearson
Reading list for this module:
CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
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PRE-REQUISITE MODULES | None |
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CO-REQUISITE MODULES | None |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Thursday 14th September 2023 | LAST REVISION DATE | Wednesday 6th March 2024 |
KEY WORDS SEARCH | Data Analysis and Visualisation |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.