Machine Learning for Big Data
This assignment for the Machine Learning and Big Data module requires students to produce a 2,000-word individual report demonstrating their understanding and practical application of machine learning techniques to big data. The assessment is worth 15 credits and is structured around five interconnected areas: data in big data, machine learning architecture, model deployment, model evaluation, and the machine learning lifecycle. The first part focuses on identifying and evaluating suitable datasets for a selected big data topic and determining whether the datasets are appropriate for the intended machine learning application. Students are expected to examine the characteristics of their data and apply appropriate pre-processing approaches, including consideration of attribute selection and data preparation. The second part addresses machine learning modelling architecture. Students must develop an appropriate architecture for their big data application and may compare alternative architectural approaches. The report should explain the selected machine learning techniques and demonstrate how they operate as part of the proposed system. Practical considerations such as performance, scalability, fault tolerance, technology usage and reliability should also be considered. The third part requires students to implement and deploy the proposed data and machine learning model. This includes testing, visualising and evaluating the resulting outcomes. The fourth part requires critical evaluation of the dataset selection, modelling design, implementation and application, including assessment of whether the selected machine learning techniques are appropriate for the intended purpose. The final part focuses on the complete project lifecycle. Students are expected to critically reflect on the work undertaken, identify what they have learned, evaluate the development process and explain how the machine learning application could be improved in a future implementation. The assessment develops five learning outcomes covering big data sources and applications, machine learning techniques, practical application of machine learning tools, critical evaluation of techniques and tools, and the ability to follow a complete big data analysis lifecycle. The marking criteria allocate 20% to each of these five areas. The assignment is submitted as an individual written report. The brief states that Microsoft Word should be used rather than PDF and requires students to acknowledge sources and any AI tools used in accordance with the stated AI policy.
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