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Assessment Item 2 of 2 Briefing Document
Title: CMP3751M Machine Learning Indicative Weighting: 50%
Learning Outcomes
On successful completion of this component a student will have demonstrated competence in the
following areas:
• LO1 Critique and appraise the scope and limits of machine learning methods by identifying
their strengths and weaknesses
• LO2: Using a non-trivial dataset, plan, execute and evaluate significant experimental
investigations using multiple machine learning strategies
Task Overview: Classification of Pressurised Water Reactor Status
The objective of this assignment is to analyse a dataset
concerning pressurised water reactor data, specifically
on the properties involved in the fuel assemblies
cluster vibrations, alterations of thermal and hydraulic
parameters, etc. For over 70 years, the nuclear power
industry – in the UK and worldwide – has primarily
focused on the technological evolution of reliable
nuclear power plants to produce electricity. By
monitoring pressurised water reactors (a type of
nuclear reactors) while running at nominal conditions,
it is possible to collect valuable insight and extract
knowledge for early detection of abnormal events.
Various types of fluctuations and perturbations can be
caused by the turbulent nature of flow in the core,