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

Discipline Specific Skills and Knowledge

Personal and Key Transferable / Employment Skills and Knowledge

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.

Basic reading:

• Bishop, C. (2007 Pattern Recognition and Machine Learning, Springer.
• Webb, A. (2002) Statistical Pattern Recognition, 2^nd 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: