MACHINE LEARNING/AI AND VISUALISATIONS - 2026/7

Module Overview

This module provides an applied introduction to machine learning and artificial intelligence (AI) within a business analytics context. It focuses on the design, implementation, evaluation, and interpretation of analytical models for data-driven decision-making.

Students will develop knowledge of statistical and algorithmic machine learning methods, including supervised, unsupervised, and potentially reinforcement learning approaches. The module emphasises practical implementation using real-world datasets and modern data science workflows.

Key topics include data preparation, model development, validation techniques, and ensemble methods. Neural networks, optimisation, and explainable artificial intelligence (XAI) may be considered. Students will critically evaluate analytical approaches and interpret model behaviour using appropriate evaluation and explainability techniques.

The module also discusses emerging developments in AI (e.g. generative AI and large language models) and considers their opportunities and limitations in business applications.

Module provider

Surrey Business School

Module Leader

GARN Wolfgang (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

This module requires MANM530.

Module content

Indicative Content

• Foundations of Machine Learning and Artificial Intelligence: machine learning workflows, AI concepts, generative AI and large language models
• Data Preparation for Machine Learning: preprocessing, feature engineering, class balancing, dimensionality reduction (e.g. PCA)
• Statistical Machine Learning: linear regression, logistic regression, confusion matrix, performance metrics, holdout and cross-validation methods
• Supervised Learning: decision trees, ensemble methods (boosting and random forests), neural networks (MLP), gradient descent
• Explainable Artificial Intelligence (XAI): feature importance, model interpretation, transparency and trust in AI systems
• Unsupervised Learning: association rules (Apriori), clustering (k-means, hierarchical clustering, self-organising maps), similarity measures
• Optimisation and Reinforcement Learning: knapsack problem, genetic algorithms, Pareto efficiency, agents, policies, Markov Decision Processes, Q-learning
• Model Evaluation and Decision Support: model comparison, robustness, overfitting, interpretability, business implications

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to evaluate students¿ ability to apply machine learning and AI techniques within a structured analytics framework and communicate insights effectively to a business audience.

Summative Assessment

Coursework (100%) ¿ Group Business Analytics Project, consisting of:

• Written report
• Oral presentation
• Submission of source code

Students will analyse a real-world dataset and develop a data-driven solution following the CRISP-DM methodology. Students must implement and evaluate machine learning models and demonstrate the ability to generalise results to unseen data.

Formative Assessment

Students will complete weekly lab-based exercises involving data analysis and machine learning techniques. These activities provide ongoing opportunities for feedback and skill development in preparation for the summative assessment.

Feedback

Formative feedback is provided during laboratory sessions and through guided exercises. Summative feedback will be provided on all assessed components, addressing:

• Analytical approach and technical implementation
• Model selection and evaluation
• Interpretation of results
• Quality of communication and visualisation
• Individual contribution

Module aims

• Develop a systematic understanding of machine learning and artificial intelligence methods for business analytics and decision-making
• Enable students to design, implement, evaluate, and interpret analytical models using real-world datasets and appropriate data science workflows
• Equip students with the ability to critically compare machine learning approaches using validation, performance evaluation, and explainability techniques

Learning outcomes

Methods of Teaching / Learning

The module is delivered through a combination of:

• Lectures introducing key concepts and methodologies,
• Computer laboratory sessions focusing on practical implementation and experimentation,
• Guided independent study using datasets, coding exercises, and online materials,
• Formative activities based on real-world business analytics problems.

Students are expected to engage in independent study, including reading academic and practitioner literature, implementing analytical methods, and evaluating machine learning models using appropriate programming tools.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM547

Other information

Employability: students develop highly sought-after machine learning and AI skills that they apply across a range of business contexts.

Global and Cultural Capabilities: This module offers students the opportunity to solve machine learning and AI challenges without geographical restrictions.

Digital Capabilities: Throughout the module, students learn to navigate and use Data Science languages to analyse data in a business context. The analysis will use machine learning and AI techniques, covering aspects of the CRISP-DM cycle (or equivalent), creating algorithms.

Resourcefulness & Resilience: students demonstrate the capability to work on a substantial piece of coursework, which will involve initiative, challenges, and opportunities to demonstrate their creativity and an ability to adapt data analysis to the chosen business context.

Sustainability: The module aims to develop students' understanding, awareness, and capability to synthesise and develop innovative, sustainable solutions using machine learning and AI techniques to generate actionable insights.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.