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Research Methods
Assignment 3 – Large Language Models: LLM Coding and Report
This individual assessment for the Research Methods module focuses on the application of Large Language Models (LLMs) to a practical data science problem. The assignment is worth 25% of the module and is designed to develop students’ knowledge and understanding of research methods, investigative planning, data analysis, model evaluation and effective technical communication. Students are required to complete both a coding component and a concise written report demonstrating how an LLM has been selected, trained or fine-tuned, applied to a suitable task and evaluated against an appropriate baseline. The assessment begins with familiarisation with relevant literature. Students are expected to investigate the history and development of their chosen problem and examine the methods that have previously been used to address it. The brief provides key papers on common types of LLMs as a starting point for the literature review. Students then select a task that can be addressed through fine-tuning an LLM, with examples including sentiment analysis, fake news detection and topic classification. A publicly available dataset suitable for the selected text-classification problem must also be identified. Suggested sources include Kaggle and Hugging Face Datasets. The data must be appropriately preprocessed, including tokenisation using BERT's tokenizer and division into training and testing sets. Students then fine-tune a pre-trained BERT or BERT-style model using suitable tools such as the Hugging Face Transformers library and PyTorch. Possible model choices include BERT, RoBERTa and T5. The selected model should be appropriate for the specific problem, recognising that different language models may perform differently across tasks. Students are expected to implement a suitable training process using an appropriate optimiser and loss function. Model performance must be evaluated using relevant classification metrics, including accuracy, precision, recall and F1-score. The performance of the selected LLM should also be compared with a baseline model, such as Logistic Regression, Naive Bayes or a pre-trained BERT model. The analysis should explain the results and consider their relevance to the chosen problem. Two main submission components are required. The first is a code notebook, such as a Jupyter or Google Colab notebook, containing annotations explaining the purpose and operation of the relevant code so that another person can understand and reproduce the work. The second is a report of no more than three pages, including appropriate figures, tables and references. The report should cover the motivation and dataset, methodology, model training and evaluation, results and discussion, limitations, conclusion and possible future improvements. The assessment rubric places particular emphasis on coding quality and implementation, model architecture, analysis and interpretation, and report presentation. Strong work should demonstrate well-structured and reusable code, clear explanation of the model architecture and configuration, appropriate evaluation metrics and visualisations, meaningful comparison with relevant literature or baseline models, and critical evaluation of the model's success and possible improvements.
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Artificial Intelligence / Natural Language Processing / Deep Learning
Applying Advanced AI Methods for Analysing Text Documents
This Advanced Artificial Intelligence coursework requires students to implement and evaluate natural language processing, natural language understanding and neural-network techniques for analysing text documents. The assessment uses a supplied social-media dataset containing more than 89,000 posts linked to 947 news headlines, with each post labelled as real or fake through a combination of headline ground truth and majority-vote annotation. Each social-media post is treated as an individual text document for classification and topic-analysis purposes. CMP_6059B_7059B_2025_26_CW1-pre… The first major task focuses on identifying fake text documents. Students must preprocess the text using appropriate NLP techniques, transform documents into numerical feature representations and experiment with alternative preprocessing approaches to determine which performs best. A separate unseen test set must be reserved to evaluate generalisation, while the remaining data is used for training both shallow and deep neural-network classifiers. Students are expected to explain and justify how the data is split. CMP_6059B_7059B_2025_26_CW1-pre… Students first design a multi-layer perceptron (MLP) capable of predicting whether documents are real or fake. The architecture must be justified in terms of input dimensions, number of layers, neuron counts, activation functions and outputs. A second deep-learning neural network must then be developed for the same classification problem, with justification of the chosen network structure, layer types, activations and other configuration decisions. CMP_6059B_7059B_2025_26_CW1-pre… For both networks, students train baseline models and select three hyperparameters considered most important for improving performance. These hyperparameters must be tuned systematically, with visuals prepared to show the experimentation process and resulting performance changes. Appropriate evaluation metrics are then used to compare the trained models. The strongest MLP and deep-learning models must be saved so that they can be loaded and tested on unseen data during the final demonstration without retraining. CMP_6059B_7059B_2025_26_CW1-pre… The second task focuses on topic discovery using NLP and NLU techniques. Students perform syntactic preprocessing such as tokenisation, stop-word removal and lemmatisation or stemming, and experiment with at least two different text-representation approaches. Suggested methods include Bag of Words, TF-IDF, LDA, word vectors and word embeddings. Students must interpret the discovered topics and explain how those topics relate to document content, linked news headlines and class labels. The best topic-discovery model or models must also be saved for live analysis during the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… The assessment is completed through a bench demonstration, supported by a maximum of seven PowerPoint slides. The slides should document the system design, model-improvement process, performance evaluation and discussion of results for both fake-document classification and topic discovery. Students also submit a ZIP file containing only their Python source files. The demonstration lasts up to 15 minutes, consisting of approximately 10 minutes for presentation and technical demonstration followed by 5 minutes for questions and transitions. CMP_6059B_7059B_2025_26_CW1-pre… The marking scheme allocates 45% to fake-document identification, including descriptive analysis, preprocessing, MLP design and deep-learning design; 35% to topic discovery, including preprocessing, model development and interpretation; and 20% to the structure, organisation, professionalism and Q&A quality of the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… Important for the public Reference Library: the brief explicitly states that the use of Large Language Models or generative AI to produce any part of the submission is strictly prohibited, including code, data processing, testing, writing or PowerPoint content. Therefore, this entry should remain only a high-level public description of the assessment and should not be presented as material intended for direct student submission. CMP_6059B_7059B_2025_26_CW1-pre…
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Multimodal Sentiment Intelligence Platform for Dynamic Market Insights
This assignment presents the development of a Multimodal Sentiment Intelligence Platform for Dynamic Market Insights. The project addresses the need for real-time market sentiment analysis by combining text and visual data through an AI-driven multimodal approach. Traditional sentiment analysis methods may have limitations when dealing with diverse data modalities, and this project aims to address this gap by integrating multiple artificial intelligence and deep learning techniques. The primary objective is to develop an AI-driven platform capable of performing real-time sentiment analysis and supporting market trend prediction. The proposed system also aims to generate business intelligence insights that can support applications in marketing, finance, and customer service. The project incorporates deep learning, large language models, and multimodal fusion techniques to improve sentiment understanding. For text-based sentiment analysis, the assignment identifies BERT and GPT-2 for sentiment classification. For visual sentiment analysis, YOLOv8 is used for object detection while DeepFace is incorporated for facial emotion recognition. Feature-level and decision-level fusion strategies are applied to combine information from different modalities and improve the overall sentiment analysis process. Retrieval-Augmented Generation (RAG) is also incorporated to provide context-aware sentiment insights. The proposed platform uses Amazon reviews and IMDb reviews as text datasets. For image or video-based multimodal data, the assignment references the CMU-MOSEI dataset and an Amazon-Reddit merged reviews dataset. Overall, the work focuses on combining natural language processing, computer vision, deep learning, large language models, multimodal fusion, and retrieval-augmented generation to create a platform capable of producing dynamic sentiment and market insights.
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Digital Forensics / Cybersecurity
2,000 words
AI-Augmented Digital Forensics Workflow Audit: Feasibility and Risk Assessment of ForensiScan AI
This digital forensics assessment examines the feasibility, reliability and legal risks associated with introducing AI-assisted analysis into a conventional forensic investigation workflow. Students act as a Lead Forensic Consultant assessing a proposed black-box system called ForensiScan AI, which claims to automatically classify illicit images and identify suspicious intent within encrypted messaging applications using Large Language Models. The central objective is to determine whether the efficiency benefits of AI can be achieved without compromising evidential integrity, transparency or legal defensibility. The report maps the proposed AI system across the four stages of the NIST forensic process: Collection, Examination, Analysis and Reporting. For each phase, students identify the data entering and leaving the AI system and determine whether the technology should be used for preliminary triage or as part of final forensic analysis. A major component concerns verification and validation. Because AI systems may hallucinate or misclassify evidence, students must design a ground-truth protocol involving human verification, statistical sampling and known datasets. The assessment also investigates whether AI-generated results can be reproduced reliably when identical evidence is processed again. The report further addresses chain of custody and data integrity, particularly whether AI processing could alter timestamps, metadata or other forensic artefacts. Ethical and legal analysis covers potential model bias, language and contextual limitations, and whether AI-generated outputs could satisfy the requirements of the Daubert Test for expert evidence. Overall, the assessment combines digital-forensic architecture, AI governance, evidential integrity, model validation, legal admissibility and professional accountability. The grading criteria place particular emphasis on forensic soundness, protection against evidence alteration, critical analysis of AI limitations such as hallucination and non-determinism, and professional technical communication. Overview word count: approximately 340 words. AI-use note: the brief permits AI only for limited assistance such as brainstorming risks, structural feedback and grammar refinement. It explicitly prohibits full report generation, unverified forensic claims and using AI to substitute for the student's own final recommendation or verification protocol. Any AI use requires an appendix containing the tool, exact prompts and a human verification log.
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Computing and Digital Technologies
3,000 words
Contemporary Computing and Digital Technologies: AI Agents Hackathon Reflective Report
This postgraduate reflective assessment forms part of the Contemporary Computing and Digital Technologies module and is based on experiential learning undertaken through an AI-focused hackathon. The hackathon theme, “AI Agents Unleashed – Building the Future of Automation,” requires MSc students from computing-related disciplines to collaborate on intelligent agent-based solutions capable of automating complex tasks, solving real-world problems and supporting human decision-making. The hackathon encourages students to investigate agent-based system design, intelligent automation and responsible AI development. Potential applications include autonomous cybersecurity monitoring, multi-agent systems for information gathering and decision support, automated data pipelines, intelligent software-development assistants, and conversational systems such as virtual tutors or career coaches. Students may use code-based approaches or platforms such as Flowise, Microsoft Power Automate and Make.com, while more advanced implementations can use technologies including LangChain, AutoGen, Python-based agent SDKs, APIs and large language models. The 3,000-word Individual Reflective Report, worth 70% of the module assessment, evaluates the student's learning and professional development arising from these experiential activities. The first component is a 2,000-word Portfolio of Evidence, requiring evidence-based reflection on participation in the hackathon. Students should evaluate their leadership and teamwork competencies using concrete evidence such as screenshots, code commits and feedback while identifying key lessons for personal and professional development. They must also consider how the experience applies to future research, career development or professional practice. The remaining 1,000 words comprise a Critical Self-Reflection examining the student's personal contribution and achievement of learning-contract goals. Students are expected to critically consider challenges encountered, how those challenges were addressed, lessons learned and their development as effective collaborative team members. Overall, the assessment integrates technical experimentation, reflective practice, teamwork, leadership, professional development and responsible use of emerging AI technologies, supported by a structured portfolio of evidence
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