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Machine Learning and Deep Learning
2,000 words
Development and Evaluation of Deep Learning Models for Healthcare Classification
This individual technical assessment focuses on the design, development, analysis and evaluation of a deep learning solution for a healthcare-related classification problem. Students select one of two provided scenarios: Polycystic Ovary Syndrome (PCOS) detection using ultrasound images or heartbeat classification using electrocardiogram (ECG) signals. The objective is to develop an appropriate deep learning approach and demonstrate critical understanding of the complete machine learning workflow, from initial data exploration through to model evaluation and reflection. Students may either design and train a deep learning model from scratch or customise and fine-tune an existing pre-trained architecture. The complete work is presented through a single Jupyter Notebook integrating Python code, technical discussion, results and visualisations. The notebook must clearly define the selected healthcare problem, explain its significance, justify methodological and architectural choices, and critically evaluate the resulting solution. The first stage involves exploratory data analysis and preprocessing, including investigation of class distributions, data imbalance and relevant patterns. Students prepare the data through techniques such as normalisation, augmentation, train-validation-test splitting and appropriate handling of class imbalance. This is followed by model design, training, validation and hyperparameter tuning, with the architecture selected according to the characteristics of the data and classification task. Model performance must then be evaluated using appropriate classification measures, including precision, recall, F1-score, ROC curves and area under the curve (AUC). The developed model should also be compared against suitable benchmark approaches, which may include traditional machine learning algorithms or alternative deep learning architectures. This comparison should identify the relative strengths and limitations of the proposed solution. The final component requires clear visual presentation and critical reflection on the complete modelling process, including limitations, challenges and opportunities for improvement. Importantly, grading prioritises methodological rigour, analytical depth and critical evaluation rather than simply achieving the highest predictive accuracy. Overview word count: approximately 330 words. AI restriction: this brief only permits automated AI tools for spelling and grammar checking. It explicitly prohibits tools such as ChatGPT, Gemini or Copilot from authoring assessment text or code; any permitted AI use must also be acknowledged.
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Data Science / Deep Learning
3,000 words
Advanced Research Topics (7PAM2016) — Building GANs from Scratch and Applying Them to Medical Imaging, Network Traffic and Sketch Generation
This Masters-level assessment asks for a complete generative adversarial network study, delivered as an annotated code submission carrying sixty per cent of the marks and a six-to-eight page technical report carrying the remaining forty. The work spans four separate GAN implementations, moving from a controlled synthetic setting into three contrasting real-world application domains. Part one builds a GAN from scratch in PyTorch on synthetic two-dimensional data. The tutorial sine-wave generator is reproduced first as a baseline, then a new distribution is modelled — a noisy parametric curve of the form y = sin(2x) + 0.3cos(5x) with an additive noise term — before the architecture itself is varied. Activation functions and layer depth are altered systematically and the resulting sample distributions plotted against the originals, so the effect of each architectural choice on convergence and sample fidelity can be seen rather than asserted. Part two applies the same principles at scale across three domains. The medical strand trains a DCGAN on the OCTMNIST subset of MedMNIST, generating synthetic optical coherence tomography retinal images, tracking generator and discriminator losses across training, and evaluating output both visually and quantitatively using Fréchet Inception Distance. A conditional GAN extension conditions the generator on class label so that images for a chosen retinal pathology can be produced on demand. The cybersecurity strand shifts from images to feature vectors, using preprocessed CICIDS 2017 network intrusion data. Benign and DoS traffic is combined and explored for class balance, a GAN is built to synthesise tabular feature vectors rather than pixels, and real against generated distributions are compared through PCA and t-SNE projections, with a discussion of how well the model generalises across attack types. The creative strand trains a DCGAN on the QuickDraw 'birthday cake' sketch category, tracking visual outputs epoch by epoch and benchmarking generated sketches against real ones, with an extension covering additional categories of differing sketch complexity. The accompanying report explains the analysis steps and the reasoning behind each architectural decision rather than restating textbook definitions of the method. It gives brief descriptions of the models used, presents generated samples and loss curves as figures, interprets the evaluation metrics, and reflects honestly on failure modes — training instability, mode collapse, and the visible flaws in synthetic output that determine whether such data is fit for downstream use. The code is written as reusable functions, commented for a reader other than its author, and reproduces every figure and numerical value quoted in the report.
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Artificial Intelligence
2,000 words
End-to-End Applied AI Development — Comparative Machine Learning and Neural Network Modelling on a Public Dataset
This assessment runs a complete applied AI development cycle end to end: problem definition, dataset selection, preprocessing, model building, optimisation, evaluation and critical reflection. Students identify a real-world problem themselves, formulate a research question from it, and source a suitable dataset from a recognised public repository such as UCI, Kaggle, Data.gov or OpenML. Dataset choice carries more weight than students expect. It must be genuinely suitable for supervised learning, complex enough to make preprocessing and feature engineering meaningful, and — critically — structured so that a traditional machine learning approach and a deep learning approach can be sensibly compared on it. A dataset too small or too clean makes the neural network component pointless; one too large or too noisy makes the whole pipeline unfinishable within the page limit. The source must be referenced and the choice explicitly justified against the research problem. The modelling requirement is fixed: at least two supervised machine learning models, plus one artificial neural network built in a mainstream deep learning framework, all trained and tested. The comparison between them is the analytical core of the work. Reporting that the neural network scored higher is not an answer; explaining why, in terms of the data's structure and each model's inductive assumptions, is. Marks are distributed across problem framing, the traditional models, the deep learning model, evaluation and critical analysis including responsible AI considerations, and academic communication. That responsible AI component is easy to overlook and is not decorative — it asks what the model's limitations mean for anyone who might rely on it. Presentation requirements are specific. The report is page-limited rather than purely word-limited, and every plot must be described in the text while also being legible enough to communicate on its own — a common failure is dense default library output pasted in without axis labels or scale. The implementation is documented in a notebook combining markdown and code cells so the development process is visible, not just the final result, and submissions typically include the cleaned dataset alongside the code. The strongest submissions treat the notebook and the report as one argument. Weaker ones produce a working notebook and then write a report that describes it, rather than a report that uses it as evidence. Our support on assessments of this type is guidance-based. Typical areas of help include: advising on whether a candidate dataset can actually support the required model comparison, explaining how to justify preprocessing decisions, clarifying which evaluation metrics suit which problem type and why accuracy alone is often misleading, showing how to structure a critical limitations and responsible AI discussion, checking Harvard referencing, and reviewing a student's own draft against the published marking criteria.
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