Module manager: Professor Danat Valizade
Email: d.valizade@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
| COMP5611M | Machine Learning |
| GEOG5304M | Machine Learning for Environmental Data |
| MATH5743M | Statistical Learning |
| SOEE5980M | Machine Learning and Statistics |
This module is not approved as an Elective
Machine learning is reshaping how organisations compete, make decisions and create value. Understanding this technology is essential for future business leaders. This module aims to develop students’ critical understanding of machine learning as a core business technology and managerial resource. Students will gain a solid foundation in algorithms such as regression models, ensemble algorithms, clustering and neural networks. Through a dynamic blend of cutting-edge research, practical case studies and business simulations, students will learn how to critically evaluate machine learning algorithms and apply them strategically and ethically within diverse organisational contexts.
The module is designed to enable students without a quantitative or computer science background to engage confidently with key machine learning concepts, models, and applications commonly used in organisational settings.
The module introduces foundational machine learning approaches and supports students in developing the analytical capacity to interpret model outputs, evaluate their limitations, and assess their strategic and organisational implications. Learning activities emphasise the responsible and ethical use of machine learning, including issues of transparency, explainability, bias, and accountability.
Students will be equipped to evaluate and effectively communicate machine learning insights to diverse groups of stakeholders to make informed judgements in business and policy contexts.
On successful completion of the module students will be able to:
1. Demonstrate understanding of the fundamental principles and assumptions underlying machine learning approaches used in business contexts.
2. Evaluate the appropriateness of different machine learning models for addressing specific organisational and managerial problems.
3. Use Generative AI tools ethically to unpack complex machine learning outputs and turn them into actionable business insights.
4. Critically assess implications of machine learning solutions, including risks related to data security, bias, governance, and wider environmental concerns.
5. Integrate cutting-edge academic research to understand how machine learning methods can support organisational decision-making.
On successful completion of the module, students will be able to:
1. Digital, technical: Apply critical thinking and analytical skills to interrogate data-driven claims and algorithmic outputs.
2. Enterprise, work-ready: Communicate complex machine learning concepts and findings clearly and effectively to specialist and non-specialist audiences.
3. Academic, work-ready: Synthesise academic research, real-world data, and specific business contexts to support evidence-based, transparent decision-making.
4. Sustainability: Demonstrate ethical awareness and professional responsibility in evaluating the design and use of machine learning algorithms.
The module syllabus covers the machine learning lifecycle and core techniques including supervised and unsupervised learning, ensemble methods, and neural networks, with a focus on how these models are developed, evaluated, and applied in diverse business contexts. It also examines model interpretability and issues of bias, governance and responsible AI deployment. Students will learn to translate machine learning outputs into strategic insights and evidence-based decisions through case studies and simulation-based exercises.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 6 | 1.5 | 9 |
| Seminars | 10 | 1 | 10 |
| Practicals | 3 | 1 | 3 |
| Private study hours | 128 | ||
| Total Contact hours | 22 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Students will be supported through structured formative feedback opportunities, including:
1. An early diagnostic exercise focused on interpreting and critically assessing outputs from a simple machine learning model.
2. Optional submission of a draft executive briefing, with feedback focused on analytical direction, depth, and coherence.
3. Peer feedback activities on draft executive briefings, aligned with the summative assessment criteria.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Presentation | A boardroom simulation. Students are divided into teams and presented with a company pack, including a company description, statement of the problem, and a data pack with machine learning outputs produced by the data team. Students will need to prepare for a boardroom meeting, present and justify potential solutions and associated risks. | 40 |
| Report | A 1,500-word professional briefing for the boardroom meeting based on the company case, providing a detailed interpretation of the data with recommendations grounded in the company’s context and machine learning outputs. The briefing should include a critical reflection log outlining how Generative AI tools were used their benefits and limitations, and the points at which human judgement, contextual reasoning and ethical evaluation were essential. | 60 |
| Total percentage (Assessment Coursework) | 100 | |
The resit for this module will be 100% by a 5-minute video presentation for the executive board meeting and a 1,500 word report.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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