Run by School of Computer Science and Electronic Engineering
20 Credits or 10 ECTS Credits
Overall aims and purpose
To introduce the fundamentals of machine learning which include basic and advanced classification methods, clustering and feature selection. To enable the students to apply some of the learned methods to real data sets.
Indicative content includes:
- Basics of machine learning: Concepts of object, class, feature. Training and testing protocols. Error estimation. ROC curves. Supervised and unsupervised learning.
- Classification methods: basic classifiers and classifier ensembles.
- Feature selection.
- Neural networks: standard architectures and deep learning.
Equivalent to the range 60%-69%. Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.
Equivalent to the range 70%+. Assemble critically evaluated, relevent areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.
Equivalent to 40%. Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.
Explain and apply the basic notions and principles of machine learning.
Summarise neural network models and their training procedures.
Apply feature selection methods with different classifiers.
Compare and contrast different classifier models and their operation.
Detail and apply clustering algorithms to data sets.
Teaching and Learning Strategy
2 lectures per week x 12 weeks
24 hours over 12 weeks (2 hours per week) including 72 hours for preparation.
Self-study. Revision after the lectures. Preparation for the exam and writing the assignments.
- Numeracy - Proficiency in using numbers at appropriate levels of accuracy
- Computer Literacy - Proficiency in using a varied range of computer software
- Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
- Exploring - Able to investigate, research and consider alternatives
- Information retrieval - Able to access different and multiple sources of information
- Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
- Presentation - Able to clearly present information and explanations to an audience. Through the written or oral mode of communication accurately and concisely.
Subject specific skills
- Knowledge and understanding of facts, concepts, principles & theories
- Use of such knowledge in modelling and design
- Problem solving strategies
- Development of general transferable skills
- Methods, techniques and tools for information modelling, management and security
- Knowledge and understanding of mathematical principles
- Knowledge and understanding of computational modelling
Resource implications for students
Courses including this module
Compulsory in courses:
- H116: BSc Applied Data Science (Degree Apprenticeship) year 3 (BSC/ADS)
- H113: BSc Data Science and Machine Learning year 3 (BSC/DSML)
- H117: MComp Computer Science year 3 (MCOMP/CS)