Module Catalogue 2025/26

Pre Requisite Comment

Basic knowledge of Python programming (including numpy and pandas), statistics, linear algebra, and calculus.

Co Requisite Comment

N/A

Aims

Machine Learning is concerned with the design of algorithms for recognising patterns in data. The field of pattern recognition represents the basis for a wide range of applications for automatic data analysis, such as computer vision, automatic speech recognition, or activity recognition primarily based on sensor-based observations of humans in their environment. The growth of “big data” means that such analysis techniques are now widely used for mining information from large amounts of data as they are collected in contemporary computing infrastructures, including clouds.

Conceptually, Pattern Recognition aims for the detection of instances of relevant classes that are typically associated with reappearing patterns in data streams. Examples of which are the automatic detection of faces in video streams, automatic transcription of spoken language, analysis of human movements, trend prediction in stock market data, intrusion detection in computer systems, or the analysis of social networks. The task is to find, model (or "learn") and classify those patterns, and to distinguish relevant from irrelevant events.

Machine Learning techniques represent the algorithmic foundation for such tasks and involve both statistical modelling techniques and probabilistic reasoning approaches.

This module aims to provide a foundation in the field of Pattern Recognition and an expertise in Machine Learning techniques as a toolkit for automatically analysing (large amounts of) data – be it static data, such as images, or dynamic data, such as time series and sensor data.

Outline Of Syllabus

• Paradigms of Machine Learning.
• Exploratory Data Analysis.
• Experimental Design.
• Standard algorithms for classification, regression, and clustering.
• Natural Language Processing.
• Data pre-processing.
• Interpretability, fairness, and ethics of Machine Learning.

Intended Knowledge Outcomes

To be able to:

• Build on the algorithmic foundations of statistical pattern recognition and machine learning approaches and
their integration into practical analysis systems.

• Discern the capabilities of different modelling and analysis approaches, which allows for informed decisions
regarding the suitability of particular recognition and learning techniques.

• Exploit the potential of pattern recognition and machine learning techniques for real-world applications.

Intended Skill Outcomes

• The ability to appreciate, analyse and use the structure and algorithmic foundations of statistical pattern
recognition and machine learning systems.

• The ability to apply pattern recognition and machine learning techniques to real-world analysis problems.

Teaching Rationale And Relationship

Lectures (in person) with additional pre-recorded materials provides maximum flexibility for students learning new material.

Practical classes (in person) allow students to check their understanding and gain support for the material.

Assessment Rationale And Relationship

Coursework assessments are through individual deliverables, emphasising both the conceptual and applied nature of the module. Students will work on a practical recognition task, where they will set up and evaluate a machine learning system that fulfils certain specified criteria.

For the extended technical project, the student can choose one from the existing project pool or define their own project. This project can assess the students’ modelling skills when facing real-world challenging problems.

Through this assessment, the student can be assessed on their understanding of machine learning, data processing skills, tools as well as scientific writing.

General Notes

N/A