Module manager: Dr Shabbar Naqvi
Email: S.Naqvi@leeds.ac.uk
Taught: 1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr), 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr) View Timetable
Year running 2026/27
This module is not approved as an Elective
This module introduces the fundamental principles and techniques of classical machine learning, with an emphasis on statistical approaches to learning from data. It explores how models capture patterns, make predictions, and generalise through both statistical and non-statistical methods, including regression, classification, clustering, and ensemble techniques. Students develop practical intuition and conceptual understanding of how different learning paradigms operate, preparing them to analyse and interpret model behaviour across diverse data-driven contexts.
This module aims to develop a deep understanding of the principles, assumptions, and methodologies that underpin classical machine learning. Emphasis is placed on statistical approaches while also introducing non-statistical techniques that demonstrate the diversity of models used to learn from data. Students explore how algorithms represent relationships, classify observations, and uncover structure through both supervised and unsupervised learning. The module also aims to establish conceptual bridges between classical machine learning and the more advanced approaches encountered in deep learning and generative artificial intelligence (GenAI), showing how foundational ideas in representation, probability, and structure carry forward into modern modelling paradigms. Learning activities integrate conceptual explanations, worked examples, and guided exercises that bridge theory and practice, allowing students to investigate model behaviour, interpret outcomes, and evaluate performance. Through this progression, students build the confidence and analytical fluency needed to apply machine learning methods critically and effectively in varied data contexts.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Explain the fundamental principles and assumptions that underpin classical machine learning methods.
2. Apply statistical and algorithmic techniques for classification, regression, clustering, and dimensionality reduction.
3. Evaluate model performance and interpret outcomes using appropriate analytical and diagnostic approaches.
4. Compare alternative learning paradigms to understand their suitability, strengths, and limitations across different data contexts.
5. Model data-driven relationships through the application of supervised and unsupervised learning approaches.
6. Assess how data representation, bias, and generalisation influence model behaviour and predictive performance.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Apply critical and analytical thinking to assess data-driven problems and determine appropriate approaches to their solution.
2. Demonstrate creativity and innovation in designing and refining solutions to complex, ambiguous, or open-ended challenges.
3. Communicate analytical insights and model results clearly and effectively, using suitable visual, written, and oral formats for different audiences.
4. Apply systematic modelling and integrative reasoning to design and evaluate coherent machine learning models.
5. Exercise reflective practice and self-directed learning to evaluate performance, adapt methods, and integrate new tools or techniques independently.
Indicative content for this module includes:
· Foundations of learning from data: supervised and unsupervised learning paradigms
· Statistical learning principles and generalisation from examples
· Linear and generalised linear models for regression and classification
· Non-linear and non-parametric methods including tree-based and kernel approaches
· Clustering and dimensionality reduction for exploratory data analysis
· Model evaluation, selection, and validation techniques
· Bias, variance, and interpretability in machine learning models
· Conceptual links between classical machine learning and advanced methods in deep learning and GenAI
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Discussion forum | 6 | 1 | 6 |
| WEBINAR | 6 | 1 | 6 |
| Independent online learning hours | 42 | ||
| Private study hours | 96 | ||
| Total Contact hours | 12 | ||
| Total hours (100hr per 10 credits) | 150 | ||
1. Webinar-Based Discussion and Q&A
2. Weekly Practical Exercises
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Online Assessment | ~20 questions about different scenarios | 20 |
| Coursework | Technical Report | 80 |
| Total percentage (Assessment Coursework) | 100 | |
This module will be reassessed through a 100% individual assessment in the same format as Assessment 2 (coursework project). The reassessment will involve a practical project that requires students to apply and integrate the knowledge and skills developed across all learning outcomes.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team