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Machine Learning / Cloud Computing
2,000 words
Technical Evaluation and Professional Reflection on a Cloud-Based Machine Learning Fraud Detection Solution
This postgraduate individual assessment critically evaluates a cloud-based machine learning solution for financial fraud detection developed as part of a preceding group project. The scenario concerns a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and strengthen customer security. The individual report requires students to examine both the technical quality of the developed solution and their own professional contribution to the project. NUL - LD7187 -Assessment Brief … The first component, Technical Evaluation, accounts for 50% of the assessment and has a suggested allocation of approximately 1,000 words. Students critically analyse the group solution with particular attention to data preprocessing, machine learning model choices, evaluation metrics and technical limitations. The analysis should go beyond description by explaining how specific modelling and design decisions influenced the final outcomes of the fraud-detection system. NUL - LD7187 -Assessment Brief … The second component, Reflection and Professional Issues, also accounts for 50% and is approximately 1,000 words. Students critically reflect on their personal contribution, teamwork experience, challenges encountered and lessons learned during the project. The discussion also addresses wider professional and ethical considerations associated with AI and cloud-based machine learning, including bias, fairness, sustainability and data privacy. NUL - LD7187 -Assessment Brief … The assessment is designed to demonstrate critical understanding of machine learning methods, cloud-computing architectures, practical development of machine learning solutions and awareness of the social, ethical and sustainability implications of AI technologies. A Seminar Activity Tracker must also be included as an appendix to provide evidence of weekly participation and knowledge development. NUL - LD7187 -Assessment Brief … NUL - LD7187 -Assessment Brief … Higher-level performance requires a comprehensive connection between technical decisions and outcomes alongside deep reflection on teamwork, professional development, ethics, fairness and sustainability. NUL - LD7187 -Assessment Brief … Overview word count: approximately 300 words. AI-use note: the brief permits AI for limited support such as grammar improvement, structure, organising ideas and suggestions. The student's main content, analysis and conclusions must remain their own, and any AI use must be declared. NUL - LD7187 -Assessment Brief …
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Computing
Independent Critical Analysis 30
Independent Critical Analysis 30 is an individual assessment that develops students' ability to critically analyse ethical, social, legal and professional issues arising from a computing-related scenario. The assessment is worth 30% of the overall module assessment and requires students to respond to three questions relating to the ethical and professional issues presented in the scenario. Students are expected to apply the specific analytical techniques or approaches identified in the assessment instructions when developing their responses. The assessment focuses on the critical examination of ethical issues and professional responsibilities within contemporary computing contexts. Students are expected to identify relevant ethical concerns and provide a reasoned justification for their analysis. The marking criteria emphasise the identification of a single ethical issue or ethical risk, followed by a structured analysis using the EHAZOP Framework. Higher-level responses are expected to provide clear justification for the ethical issue selected, examine the associated consequences, and demonstrate a systematic and critical approach to the scenario. A second component focuses on ethical risk analysis using the Ethical OS Toolkit. Students are expected to identify an ethical risk associated with the scenario and justify their selection through a structured analysis. The assessment therefore requires students to consider the potential consequences of computing-related decisions and to demonstrate an understanding of how ethical risks can be identified and evaluated. The final component addresses professional issues. Students are expected to identify relevant professional principles and apply an appropriate professional code of conduct, such as the ACM or BCS Code of Conduct, to the scenario. The analysis should explain which professional principles are relevant and provide a reasoned justification for their application. The marking criteria place emphasis on systematic analysis, appropriate justification and understanding of responsible computing practice. The assessment therefore connects ethical decision-making with professional responsibilities and standards. The assignment also assesses broader module learning outcomes relating to ethical standards, contemporary computing contexts, legal and professional issues, and the ability to critically analyse high-profile cases or case studies. Students are required to submit the provided answer sheet containing the scenario and three questions. The assessment brief states that submissions are subject to Turnitin and that students must follow the specified submission requirements.
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Technical Evaluation and Professional Reflection on a Cloud-Based Machine Learning Fraud Detection Solution
This individual technical evaluative report forms the reflective and critical component of the Machine Learning on Cloud module. It builds directly upon a group project involving the development of a machine learning solution for financial fraud detection. The report requires each student to critically evaluate the technical decisions made within the group solution while also reflecting on their individual contribution, teamwork experience and professional development. The first component, Technical Evaluation, accounts for 50% of the individual assessment and has a suggested allocation of approximately 1,000 words. Students critically examine the group’s machine learning solution, including the approaches adopted for data preprocessing, model selection and evaluation. They are expected to discuss the suitability of the chosen techniques and metrics, identify limitations and explain how individual technical decisions affected the final performance and outcome of the fraud-detection solution. The second component, Reflection and Professional Issues, also accounts for 50% and has a suggested allocation of approximately 1,000 words. Students reflect critically on their personal contribution to the project and their experience of working within a team. The discussion addresses challenges encountered, lessons learned and how the experience contributed to technical and professional development. Professional and ethical considerations form an important part of the reflection. Relevant issues include algorithmic bias, fairness, sustainability and data privacy, particularly in relation to machine learning applications within financial services and cloud environments. Students are also required to attach their Seminar Activity Tracker as an appendix, providing evidence of weekly participation and knowledge development. The assessment therefore combines technical critique with reflective practice, requiring students to demonstrate that they understand not only how a machine learning solution was developed, but also why specific technical decisions were made, their consequences, the limitations of the resulting system and the wider ethical and professional implications of deploying AI on cloud infrastructure. Overview word count: approximately 315 words. The brief also states that AI may assist with areas such as grammar, structure, organisation of ideas and suggestions, but the main content, analysis and conclusions must be the student's own work, and AI use must be declared.
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