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Machine Learning on Big Data
3,000 words
Machine Learning on Big Data using PySpark
This group assessment for the Machine Learning on Big Data module requires students to apply machine learning techniques to a large real-world dataset using PySpark DataFrame and Spark ML. The coursework is designed to develop practical understanding of big data processing, machine learning implementation, model optimisation and evaluation. Students work in groups of three or four and complete a machine learning project using a suitable large dataset obtained from a benchmarking source such as Kaggle, their workplace or another valid resource. The recommended dataset size is between 300 MB and 1 GB. The main task requires students to select a dataset containing multiple classes rather than a binary classification problem and develop an appropriate machine learning solution. The project begins with loading and preprocessing the data using PySpark DataFrame. Students are expected to address issues such as missing values, data normalisation, feature engineering and class imbalance. For text datasets, additional preprocessing techniques such as stemming, lemmatisation and TF-IDF may be applied. Students must then select and implement an appropriate machine learning method using PySpark MLlib or the Spark ML package. The brief permits approaches including multiclass classification, clustering, ensemble learning and text mining. Model parameters must be optimised using techniques such as grid search or random search. Students are required to evaluate their trained models using suitable metrics, including accuracy, F1-score, precision, recall and classification matrices where appropriate. The brief encourages students to maintain model accuracy and robustness above 90% for the highest possible mark and to discuss steps taken to address bias and variance. The results must be visualised or printed clearly, with appropriate interpretation and analysis of the findings. Students must also consider Legal, Social, Ethical and Professional (LSEP) issues throughout the project. Each student selects one LSEP principle and discusses relevant concerns such as dataset bias, privacy or ethical implications, together with suitable mitigation strategies. The final report should be approximately 3,000 words with a tolerance of ±10% and submitted as a single HTML report using the template provided on the module Moodle site. The report should consolidate the individual contributions of all group members into one comprehensive and user-friendly analytics report. The main assessment is weighted 60% for the report and 40% for the group presentation. The presentation is conducted online through Microsoft Teams, and all group members must participate. The assessment evaluates understanding of Spark, preprocessing, modelling, optimisation, evaluation and the ability to explain and interpret the implemented solution.
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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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Advanced Artificial Intelligence
CW1: Applying advanced AI methods for analysing text documents
This coursework is an individual practical assessment for the Advanced Artificial Intelligence module, focusing on the application of advanced artificial intelligence, natural language processing (NLP), natural language understanding (NLU), and neural network techniques to analyse text documents. The coursework uses a provided social-media dataset containing more than 89,000 posts associated with 947 news headlines. Each social media post is treated as an individual text document and is labelled as either real or fake according to the relationship between the linked news headline, its ground-truth status, and majority annotator agreement. The assessment consists of two main tasks. The first task focuses on the identification of fake text documents. Students are required to perform descriptive data analysis and NLP preprocessing before transforming text into numerical feature representations suitable for neural network classification. Students must design and implement both a Multi-Layer Perceptron (MLP) network and a deep learning neural network to classify documents as real or fake. The assessment requires justification of the selected architectures, including inputs, layers, neurons, activation functions and outputs. Students must also experiment with hyperparameters, evaluate model performance using appropriate metrics, compare different models, select suitable models, and save trained models for later demonstration. The second task focuses on topic discovery using natural language understanding techniques. Students are required to apply text-processing methods such as tokenisation, stop-word removal, lemmatisation or stemming and experiment with at least two different text representation strategies. Possible approaches include Bag of Words, TF-IDF, Latent Dirichlet Allocation, word vectors and word embeddings. The discovered topics must be analysed and interpreted in relation to the document content, associated news headlines, and class labels. The assessment is supported by a practical bench demonstration and a maximum of seven PowerPoint slides covering the design, model improvement process, performance evaluation and discussion of results. Students must submit their own Python code and presentation through Blackboard and demonstrate their saved models without retraining them during the demonstration. The marking scheme allocates 45% to fake text document identification, 35% to topic discovery using NLU, and 20% to the structure, organisation, professionalism and question-and-answer performance of the demonstration.
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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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Cyber Security / Penetration Testing
Web Application Penetration Testing and Security Vulnerability Assessment Portfolio
This postgraduate cyber security portfolio requires students to conduct a structured penetration test of a controlled web application and document the technical findings in a professional security-testing format. The assessment develops practical competence in identifying, validating and communicating security vulnerabilities while maintaining appropriate legal, ethical and professional boundaries. Assessment Brief CMP-L021 (PG) … Students begin by performing network and service enumeration, identifying open ports and the services running on the target host. They are expected to interpret the security implications of the findings and provide appropriate recommendations to a hypothetical client. The assessment then progresses into web-application vulnerability testing using tools such as a web browser, Burp Suite Community, Nmap and student-developed scripts. Assessment Brief CMP-L021 (PG) … A major part of the portfolio examines common web-security weaknesses including SQL Injection and Cross-Site Scripting. Students must demonstrate how they tested the application, capture relevant requests and responses, and explain the evidence supporting their conclusions. Additional tasks involve application and server reconnaissance, including identification of technologies, server versions, publicly exposed files and other information that may create security risks. Assessment Brief CMP-L021 (PG) … The higher-level reporting component requires students to document significant vulnerabilities using the conventions of a professional penetration-test report. This includes assigning CVSS scores, relating identified weaknesses to the OWASP Top 10 and NIST classifications, and supporting findings with appropriate technical evidence. Assessment Brief CMP-L021 (PG) … Students must also produce an executive summary for a non-technical audience, considering security, privacy, regulatory exposure and budget implications. A vulnerability table linking technical weaknesses with relevant regulatory concerns is also required. Overall, the assessment integrates technical penetration testing with risk communication, vulnerability classification, evidence collection and professional security reporting. Assessment Brief CMP-L021 (PG) … Overview word count: approximately 320 words. AI-use note: AI can be used in this assessment, but any use must be acknowledged and AI-generated outputs must be appropriately cited. Assessment Brief CMP-L021 (PG) …
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Big Data Analytics / Machine Learning
3,000 words
Machine Learning on Big Data Using PySpark: Large-Scale Data Analysis and Predictive Modelling
This group-based Machine Learning on Big Data project requires students to apply machine learning techniques to a large real-world dataset using PySpark DataFrames and Spark machine-learning libraries. Students select a substantial dataset, ideally between approximately 300 MB and 1 GB, from sources such as Kaggle, workplace data or other valid repositories, and develop an end-to-end big-data analytics workflow. CN7030 CRWK 26T1 The project begins with data loading and preprocessing using PySpark. Students are expected to handle missing values, perform data normalisation and feature engineering, identify class imbalance and propose appropriate mitigation strategies. Where text datasets are selected, additional preprocessing may include stemming, lemmatization and TF-IDF representation. CN7030 CRWK 26T1 The modelling stage requires implementation of an appropriate machine-learning approach using PySpark MLlib or Spark ML. The brief expects a multiclass rather than binary classification problem and allows techniques including multiclass classification, ensemble learning, clustering and text mining. Students must justify their model choice and consider model robustness, bias and variance when attempting to improve predictive performance. CN7030 CRWK 26T1 Students then perform hyperparameter tuning using techniques such as grid search or random search and evaluate the resulting model with appropriate measures. Relevant evaluation outputs may include accuracy, F1-score, precision, recall and a confusion matrix. Results should also be visualised or clearly presented and interpreted to identify meaningful patterns and performance characteristics. CN7030 CRWK 26T1 The project additionally requires consideration of Legal, Social, Ethical and Professional (LSEP) issues. Students discuss potential ethical concerns associated with their dataset, including bias and privacy risks, and propose suitable mitigation strategies. The final work is consolidated into a single user-friendly HTML analytics report that clearly presents the group's preprocessing, modelling, optimisation, evaluation and interpretation. CN7030 CRWK 26T1 CN7030 CRWK 26T1 Overview word count: approximately 335 words. If you are also uploading the presentation separately to the Reference Library, that should be a second entry under “Presentations and Academic Posters”, because the presentation forms a distinct 40% component and assesses understanding of Spark, preprocessing, modelling, optimisation, evaluation and responses to examiner questions. CN7030 CRWK 26T1
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Cyber Security / Ethical Hacking / Penetration Testing
4,000 words
Ethical Hacking and Penetration Testing: Vulnerability Exploitation, Privilege Escalation and Mitigation
This Ethical Hacking and Penetration Testing coursework requires students to conduct a practical CTF-style penetration test against a set of authorised target machines and produce a professional technical report documenting the compromise of one selected target. The assessment evaluates practical exploitation skills alongside the ability to analyse risk, explain attack vectors and recommend effective security controls. 8598ba8d8fff5a38af8d427a4ce8f84… The practical element requires students to identify vulnerabilities in multiple target systems, exploit those weaknesses to gain low-privileged access and then perform privilege escalation to obtain root-level access. Successful completion of each stage produces flags, with separate user and root flags contributing directly to the practical marks. Brief descriptions of the attack vectors and payloads used must also be recorded. 8598ba8d8fff5a38af8d427a4ce8f84… The written report focuses in detail on one compromised machine. Students must explain the reconnaissance and vulnerability-identification process, including the techniques used to discover services, web content and potential attack surfaces. The marking criteria specifically recognise appropriate reconnaissance tools such as Nmap and FFUF and reward clear justification of methods and links between reconnaissance results and identified threats. 8598ba8d8fff5a38af8d427a4ce8f84… 8598ba8d8fff5a38af8d427a4ce8f84… A further component requires a formal risk rating for the discovered vulnerabilities. Students should use a recognised risk-classification approach, such as OWASP or SANS, justify the assigned severity and discuss relevant social, legal and ethical considerations. Higher-performing work is expected to connect those considerations directly to the specific vulnerabilities identified. 8598ba8d8fff5a38af8d427a4ce8f84… The exploit section should explain the technical cause of the vulnerability, describe the exploitation process and present relevant example payloads. Mitigation recommendations must then be linked directly to the vulnerabilities discovered, with clear explanations of where the weakness occurs and how it can be remediated. 8598ba8d8fff5a38af8d427a4ce8f84… Overall, the coursework integrates reconnaissance, vulnerability analysis, exploitation, privilege escalation, risk assessment, ethical and legal considerations, technical reporting and defensive mitigation within an authorised penetration-testing environment. Important: this brief states that it is for Coventry University Group students' own use and must not be passed to third parties or posted publicly. 8598ba8d8fff5a38af8d427a4ce8f84… So for your public Reference Library, use an original summary like the one above rather than uploading the assessment brief itself.
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Artificial Intelligence / Machine Vision / Computer Vision
Artificial Intelligence and Machine Vision: Neural Network-Based Image Processing Application
This postgraduate Artificial Intelligence and Machine Vision coursework requires students to design, implement and critically evaluate a neural network-based image-processing application addressing a real-world problem. Students may select an application such as medical imaging, plant or fruit classification, skin cancer detection, object detection or image segmentation and must demonstrate an appropriate end-to-end machine vision workflow. CN7023 Coursework T2 25-26 (1) The project begins with a clear definition of the selected real-world problem, the objectives of the proposed solution and its potential practical impact. Students are expected to demonstrate creativity in selecting and designing their approach, explain the neural-network or image-processing methods adopted and justify why the selected techniques are suitable for the chosen dataset and application. CN7023 Coursework T2 25-26 (1) A substantial component focuses on simulation and implementation. Students describe the dataset, including its source, size, classes and representative images, before explaining how image data were encoded and preprocessed for use within the chosen neural network. The report must then document the selected network architecture, learning algorithm and procedures used for training, validation and testing. CN7023 Coursework T2 25-26 (1) Model performance must be communicated using quantitative and visual evidence. Required outputs include test-set accuracy, accuracy curves across training, validation and testing, and a confusion matrix supported by appropriate explanation. Students must also critically analyse the results, identify factors affecting model performance and discuss alternative methods or simulation changes that could improve the solution. CN7023 Coursework T2 25-26 (1) The coursework permits several technical routes, including combining image processing with artificial neural networks, deep learning or computer vision, or focusing on one of these approaches independently. Development may be completed using MATLAB or Python. The wider module covers artificial neural networks, CNNs, digital image processing, image restoration, compression, segmentation, classification and ethical, legal, privacy and social issues associated with AI systems. CN7023 Coursework T2 25-26 (1) Module handbook 2526-B (1) Overview word count: approximately 335 words. Important note: the coursework cover page labels the assignment as “Individual Assignment 100%,” but the module handbook clarifies that the coursework report itself contributes 50% of the module, with the remaining marks allocated to MATLAB course completion (20%), lab tasks (15%) and presentation (15%). For the Reference Library, I would use the handbook’s 50% report weighting if you need to record the assessment contribution.
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Cyber Security
Contextual Risk Assessment and Policy to Address Information Security within Supplier Agreements
This assignment focuses on the development of a contextual risk assessment and an information security policy addressing security requirements within supplier agreements for Heathrow Airport Holdings (LHR). The assessment is an individual postgraduate task worth 60% of the module and requires students to apply information security risk assessment methods, security standards and policy development techniques to a realistic organisational scenario. The assignment is based on a cyber-attack affecting Heathrow and other European airports in September 2025, where disruption to a third-party cloud-based check-in and baggage system affected airport and airline operations. The scenario highlights the security risks associated with interconnected systems, third-party suppliers and dependence on critical digital services. Students are required to assume the role of a new Chief Information Security Officer (CISO) at Heathrow Airport Holdings and investigate the organisation, its environment and the relevant threat landscape. The task requires the development of a clear organisational context, including appropriate assumptions, followed by an asset-based information security risk assessment. The risk assessment should identify and prioritise relevant risks and support the selection of controls needed to manage residual information security risks. The main policy component requires students to develop an “Information Security within Supplier Agreements” policy aligned with the ISO 27000 family. The policy should establish clear security responsibilities between LHR and its suppliers and address the protection of information assets, legal and regulatory requirements, and supplier-related security obligations. Particular attention is required for confidentiality, integrity and availability, together with ISO 27002 controls relating to information security policies and supplier agreements. The assignment also requires consideration of acceptable use of information and other assets, information classification and information labelling. The final submission consists of a cover page, context establishment, an asset-based risk assessment, the supplier information security policy, references and supporting appendices. The context establishment is limited to a maximum of two pages or 1,000 words, while the policy is limited to three pages or 1,500 words. The risk assessment is completed using the supplied template. Students are also required to provide evidence and commentary concerning the development and tailoring of the policy when using an approved AI tool, together with a self-written evaluation addressing strengths, weaknesses, privacy, GDPR and ethical considerations. The assessment is marked across context establishment, asset-based risk assessment, the information security within supplier agreements policy, and presentation, design and references. At least 20 authentic references, including standards and papers accessed through the University library, are required.
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Data Mining
Data Mining – Classification, Model Optimisation and Evaluation
This individual Data Mining assignment requires students to apply the techniques covered in the module using the WEKA data mining platform. The assessment is worth 40% and focuses on practical application of machine learning and data mining methods, requiring students to configure algorithms, analyse datasets, optimise model parameters, evaluate classification performance and explain their technical choices and results. The assignment also assesses the ability to critically evaluate different algorithms and models of data mining. The assignment includes several tasks covering different stages of the data mining process. Students are required to work with supplied datasets and use appropriate preprocessing and classification techniques. The datasets include a balanced screenshot dataset containing processed screenshots classified into categories such as "Okay" and "Bad", where the objective is to train a model capable of identifying inappropriate content. The data has been processed using PCA to provide a smaller four-dimensional representation while protecting privacy and reducing the size of the data. Another supplied dataset concerns furniture reviews, containing positive ("pos") and negative ("neg") written feedback, with the objective of building a model that can determine whether a furniture review belongs to either class. The assessment evaluates students' ability to understand and describe the datasets, including the number of instances, number of columns, data types and relevant statistical information. For text-based data, students must apply appropriate vectorisation and describe the resulting dataset characteristics. Students must also consider class imbalance and apply an appropriate method where necessary, explaining how the chosen approach affects the distribution of instances between the classes. A significant component of the assignment involves classification algorithms and parameter optimisation. The assessment requires students to work with algorithms including Naive Bayes, LibSVM and J48. Students must investigate appropriate parameters and perform parameter searches or fine grid searches to identify suitable configurations. They must explain the selected parameters, their impact on the model and the reasoning behind the chosen values. Model performance must be evaluated using appropriate validation techniques, including cross-validation. Students are required to compare the algorithms using results such as overall accuracy and confusion matrices. The assignment expects students to identify an appropriate or best-performing algorithm in the context of the dataset and to provide a clear explanation of the comparison rather than simply reporting numerical results. The rubric places emphasis on accurate configuration, clear explanation of parameter choices, dataset analysis, class-balance treatment, parameter optimisation, cross-validation and critical comparison of algorithm strengths and weaknesses. High-quality work should explain both the technical process and the implications of the results, with results presented clearly through appropriate tables, confusion matrices and graphical outputs where required. The submission must be a single PDF document containing the report and must not exceed 10 pages. Students are instructed to include their student ID at the beginning of the report but not their name or other identifying details so that marking remains anonymous. Screenshots are specifically required to demonstrate use of the student's ID number as the random seed; other WEKA results should be presented in the student's own tables or result formats. The brief also states that no research beyond the material covered in the module is required and therefore no citations or reference list are required. The assignment explicitly prohibits the use of Generative AI tools for creating content and prohibits using GenAI tools or proofreading services for proofreading. Students are expected to complete the practical work themselves and explain their own technical choices and results.
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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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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 / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assessment requires students to develop a comprehensive analytical solution to a real-world healthcare prediction problem using the WiDS Datathon 2025 Health Outcomes Prediction Dataset. The dataset contains socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents, with the principal objective of developing predictive models for ADHD diagnosis. The assessment is designed to demonstrate the complete data-science lifecycle, from data preparation and exploratory analysis through predictive modelling, interpretation and evidence-based recommendations. Students begin by exploring the dataset's features, data types and distributions before addressing missing values, outliers and other inconsistencies. Appropriate feature engineering should be undertaken where necessary, followed by Exploratory Data Analysis (EDA) using relevant visualisations to identify relationships, patterns and correlations within the data. Students with limited computational resources may use a representative subset, provided that the sampling method preserves the integrity and distribution of the original dataset and is clearly justified. A major component of the assignment involves developing and comparing at least three classification models. Appropriate techniques may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Model performance should be evaluated using measures including accuracy, precision, recall, F1-score and ROC-AUC, allowing students to identify the strongest-performing model through systematic comparison. The assessment also requires model interpretation and explainability. Students should explain the results of the selected model and may apply techniques such as SHAP or LIME to investigate feature importance and individual predictions. A feature-importance visualisation must be produced, and the most influential variables should inform practical recommendations for healthcare professionals regarding the potential use of predictive modelling in supporting earlier ADHD diagnosis and intervention. Overall, the assignment integrates data cleaning, exploratory analytics, predictive modelling, model comparison, explainable AI and research-informed healthcare recommendations. Students must submit a comprehensive report of no more than 2,500 words, alongside a Jupyter Notebook containing the implementation and outputs. The report must use Harvard referencing, with appropriate academic research integrated into the analysis, recommendations and conclusion.
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Machine Learning / Data Mining / Text Mining
Machine Learning Analysis of Classification Models and Text Mining on Furniture Review Data
This technical machine-learning report demonstrates the practical application of predictive modelling and text mining using WEKA. The work is divided into two major tasks. The first evaluates and compares Support Vector Machine and Decision Tree classification models, while the second applies text-mining techniques to furniture-review data and compares multiple classifiers after preprocessing, feature selection and class balancing. The first task uses the Screenshots.arff dataset to investigate the performance of libSVM and J48 Decision Tree classifiers. A 70% training and 30% testing split is applied, and the models are manually tuned to examine how different parameter settings affect predictive performance. For libSVM, an RBF kernel is used while different gamma and cost values are tested through grid-search-style experimentation. The report identifies gamma 0.03 and cost 2 as the strongest tested combination, producing approximately 91.67% accuracy on the test split. The J48 model is also optimised by adjusting the confidence factor used for pruning. Several confidence-factor values are examined, with 0.09 producing the strongest reported result of 80% accuracy. The optimised SVM and J48 models are then compared using five-fold cross-validation, where libSVM achieves 89.75% accuracy compared with 81.25% for J48. The second task focuses on text mining of Furniture Reviews. Text preprocessing includes TF-IDF term weighting, stopword removal, stemming, conversion to lowercase and word-count generation. The resulting textual dataset is transformed into a numerical feature representation suitable for machine-learning classification. Dimensionality reduction is performed using InfoGainAttributeEval with Ranker, selecting the 900 most informative attributes. The dataset is then balanced using WEKA techniques including Resample and SpreadSubsample to reduce class bias before classification. Finally, three classifiers—Naive Bayes, libSVM and J48—are evaluated on the balanced text dataset. The reported accuracies are 90.52% for Naive Bayes, 58.62% for libSVM and 78.45% for J48. The analysis concludes that Naive Bayes performs strongest for the processed furniture-review dataset, while the wider exercise demonstrates the importance of preprocessing, parameter tuning, feature selection, class balancing and appropriate model evaluation in producing reliable classification results. Important: this upload appears to be the completed student report, not the actual assessment guideline. Because the document does not state the university, module name, academic level, academic year, required word count or prescribed referencing style, I would leave those fields as Not specified rather than guessing.
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Machine Learning / Cloud Computing
3,984 words
Machine Learning on Cloud: Fraud Detection Model Design, Training and Evaluation
This postgraduate group project focuses on the design, development and critical evaluation of a cloud-based machine learning solution for financial fraud detection. The scenario involves a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and improve customer security. Students are required to analyse an appropriate dataset, develop a machine learning solution, evaluate its effectiveness and critically consider the suitability of cloud technologies for deployment. The project begins with a cloud feasibility study, requiring critical comparison of at least two major machine learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Evaluation criteria include performance, scalability, cost, compliance, integration and vendor lock-in, followed by a justified recommendation. Students then conduct exploratory data analysis to identify patterns, anomalies and correlations using appropriate visualisations such as heatmaps, histograms and boxplots. A substantial component addresses data preprocessing and class imbalance. Students are expected to clean and transform the data, apply scaling and encoding, perform feature engineering and investigate approaches such as SMOTE, undersampling and cost-sensitive learning. Each preprocessing choice must be justified in terms of its potential impact on model performance. Students must select and train at least two machine learning models, with suggested approaches including Logistic Regression, Random Forest, XGBoost and Neural Networks. Model development incorporates cross-validation and hyperparameter tuning. Evaluation uses fraud-relevant measures including Precision, Recall, F1 score, AUC and precision-recall curves, supported by confusion matrices, ROC curves and feature-importance visualisations. The project concludes with critical consideration of professional and ethical issues in cloud-based AI, including bias, fairness, transparency, data privacy and sustainability. The overall assessment therefore integrates cloud-platform evaluation, machine learning development, imbalanced classification, model evaluation and responsible AI practice. Overview word count: approximately 340 words.
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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assignment focuses on developing a comprehensive analytical solution to a real-world healthcare prediction problem. Using the WiDS Datathon 2025 Health Outcomes Prediction Dataset, students are required to analyse complex and high-dimensional healthcare data containing socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents. The principal predictive objective is to determine ADHD diagnosis from the available features. Students may use a representative subset of the dataset where computational resources are limited, provided that the sampling approach maintains the integrity and distribution of the original data and is appropriately justified. The assessment requires a complete data-science workflow beginning with data understanding and preprocessing. Students investigate the dataset's features, data types and distributions before addressing missing values, outliers and inconsistencies. Appropriate feature engineering should then be undertaken where it can improve the predictive capability of the models. Exploratory Data Analysis is used to identify important patterns, relationships and correlations, supported by relevant visualisations that communicate meaningful insights. A major component of the work involves the development and comparison of at least three classification models for predicting ADHD diagnosis. Suitable approaches may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Models are evaluated using performance measures including accuracy, precision, recall, F1-score and ROC-AUC, after which the most effective model is selected based on the evidence obtained. The assessment also places substantial emphasis on model interpretation and explainability. Students must interpret the selected model and may use approaches such as SHAP or LIME to explain feature importance and individual predictions. A feature-importance visualisation is required, and the most influential variables should inform practical recommendations. The final section translates analytical findings into recommendations for healthcare professionals, considering how predictive modelling could assist early ADHD diagnosis and intervention. Research literature must be integrated into the recommendations and conclusion. The assessment therefore combines preprocessing, exploratory analysis, predictive modelling, explainable AI and evidence-based healthcare decision-making within a single applied data-science project. The required report is a maximum of 2,500 words, with code, supplementary charts and tables permitted in appendices. A Jupyter Notebook containing the implementation and outputs is also required. Harvard referencing must be used throughout.
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Computer Science / Machine Learning
Machine Learning Portfolio Challenge: Support Vector Machines and Kernel Methods
This machine learning portfolio challenge requires students to demonstrate both theoretical understanding and practical application of machine learning methodologies introduced during the second block of the module. During Weeks 7–12, students select one of the machine learning methods covered in class and apply it to a dataset of their choice. The completed work contributes to an assessment portfolio and must demonstrate a clear, systematic and professionally documented experimental process. The accompanying learning material places particular emphasis on Support Vector Machines (SVMs) and kernel-based machine learning. SVMs are presented as maximum-margin classifiers that construct a decision hyperplane between classes, with support vectors playing the key role in defining the classification boundary. The material also introduces soft-margin optimisation, slack variables, the regularisation parameter C, primal and dual formulations, and the use of KKT multipliers. SVMs are additionally discussed in relation to multiclass classification through approaches including one-versus-all and Error Correcting Output Codes (ECOC). Kernel methods extend these principles by replacing explicit feature transformations with similarity functions. Students encounter concepts including Gram matrices, feature mappings, Mercer conditions and the kernel trick, alongside common kernel choices such as linear, radial basis function and polynomial kernels. The material demonstrates how kernel methods can represent nonlinear decision boundaries in the original input space while retaining a linear representation in an embedded feature space. The final submission must be produced as a single PDF lab notebook. It should contain clear and well-commented MATLAB, Python or equivalent code explaining each methodological step, experimental results presented through appropriate tables and/or plots, and narrative discussion explaining the selected approach, observations and conclusions. The notebook should integrate code, outputs and written explanation into a coherent and professional submission. Students may also optionally present their solution in class, where the quality of explanation and discussion can contribute additional marks.
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