Artificial Intelligence for Business – MSc

The aim of this module is to introduce students to the mathematical topics relevant to artificial intelligence, covering linear algebra, probability and statistics. Students will gain knowledge of vectors, matrices, eigenvectors, eigenvalues and matrix factorisation. In the probability and statistics section, students will develop skills to effectively analyse, visualise and interpret data to discover important insights. They will learn about tools to manage uncertainty, quantify relationships and make data-driven business decisions.

The purpose of this module is to expand the student’s understanding of techniques employed in AI by exposing them to real-world case studies. These case studies may be of approaches that organisations have taken to implement solutions to real problems in the field or based on scenarios which have no prior solutions to allow the students to create their own approach and compare it with other students. One of the main goals of this module will be to expose students to the varied uses of AI in different industries and critique various case studies using different applications of AI.

This module will introduce students to the classical machine learning algorithms that are used for classification, regression and pattern detection. Students will learn about the core data handling skills necessary to complete the different phases of machine learning implementation. Firstly, students will develop knowledge and skills related to data pre-processing and manipulation, Secondly, students will learn about different machine learning algorithms and the scenarios in which each is appropriate to use. Students will develop the skills to build models and assess their predictive performance. Finally, students will interpret and communicate the results achieved. A goal of this module is to expand the student’s knowledge of classical predictive techniques employed in business contexts by exposing them to case studies of approaches that organisations have taken to implement solutions to problems in the field.

This module introduces data mining and business intelligence concepts. Association rule mining, clustering, recommender systems, and timeseries forecasting are the basic concepts covered in this course. These techniques can provide insight into product and customer similarities, provide product & content recommendations, and facilitate price and sales forecasting efforts. Where possible, code for the approaches employed will be generated using large language models, with students receiving demonstrations on this.

Much of the data produced today is unstructured, such as social media posts, text documents, images and video. Extracting values from unstructured data requires additional tools and techniques, compared with those required to analyse structured datasets. This module explores the theory and practice of managing data, including identifying and extracting data, data pre-processing, transforming and loading data for analysis. A variety of analytics tools and techniques needed to gain value from unstructured data will be employed, with a particular focus on the practical analysis of textual data.

This module will build on the foundations laid by the Fundamentals of Machine Learning module and give students a broad overview of the key conceptual ideas and practical skills (Python) necessary to work effectively with deep learning and modern generative AI technologies.

This module will provide students with an insight into the legal, ethical and social implications of Artificial Intelligence.

This module aims to introduce students to the key concepts involved in research and to develop their understanding of the uses and relevance of the major methodologies employed. The material covered in this module will form the basis for the Applied Research Project element of the MSc programme, with one of the key outcomes of this module being a valid and robust proposal for an applied research project.

The project builds on the Research Methods module, where the research proposal will have been developed and submitted. The dissertation will consist of 20,000 words, excluding bibliography and appendices, providing students with an opportunity to develop and demonstrate programming and artificial intelligence model use for business along with written communication skills. As the capstone component of the programme, this element will help integrate the curriculum content and deliver a significant body of work that can contribute to the body of knowledge in artificial intelligence for business. The dissertation will draw on analytical and evaluative competences based upon knowledge and skills developed during the programme. The exercise also provides an opportunity for students to develop their interests in a particular area of artificial intelligence, while demonstrating an ability to undertake independent research in an ethical and methodologically sound manner. As with any project of significant complexity, students will have opportunities to hone time management, problem-solving, and planning skills throughout the course of the project.