7044SCN Machine Learning and Statistical Modelling CW Assignment Brief Nov 2025 | CU

Assignment Information

Module Name: Machine Learning and Statistical Modelling

Module Code: 7044SCN

Assignment Title: Coursework

Assignment Due: Monday, 15/12/2025, at 6pm UK time

Assignment Type: Applied Core Assessment

Assignment Task

Machine learning algorithms for solving real-world challenges by considering statistical aspects

During this module, you learned about different machine learning techniques and statistical modelling, associated concepts and applications. We explored a number of classification/regression algorithms, such as Logistic Regression, Linear Discriminant Analysis (LDA), Optimized K-nearest Neighbour, Bayesian and Statistical Methods, Support Vector Machines, Naïve Bayse Classifier, Bayesian Networks and Decision Trees, Linear Regression, Regularization methods and Gaussian Process Regression delivered in this module. Also, we covered clustering algorithms, such as K-means, K- median, Topic Modelling and feature selection and extraction methods, such as PCA.

In this assignment, you will have to select an application related to a classification or regression or clustering and explore how best to apply machine learning algorithms with focus on statistical concepts, learned during this module, to critically examine and solve it.

Feel free to select from the provided datasets or, upon agreement with your module leader, choose an alternative dataset. Your task is to apply a minimum of FIVE classification, regression or clustering techniques to analyse the chosen dataset. Also, discuss the statistical aspects of the techniques you would like to apply.

1.    Bags of Words  (Classification)
2.    Daily and Sports Activities Dataset (Classification and Clustering)
3.    Dresses Attribute Sales Dataset (Regression)
4.    Or other (choose as you wish, but match techniques with the dataset)

Notice: The example datasets used in the labs are NOT allowed. We also encourage you to use unstructured dataset (e.g. signal data).

You can combine and choose from the above-mentioned algorithms or you can use or come up with a new classification or clustering algorithms.

• Examine the fundamental concepts of machine learning and statistics, their implementation and application.
• Perform appropriate preparation of a dataset and evaluate the performance of different learning algorithms on this dataset with focus on statistical concepts.
• Gain practical experience in selecting machine learning algorithms for solving a real-life classification or clustering problem.
• Write up your own (using your own expression for describing the deliverable and emphasizing your contributions as well). Contact the lecturer if you have doubt about this.
• Welcome to submit progress on your work regularly to get formative feedback and improve the final submission.
• Before your start, READ some provided samples at the bottom of “Module Essentials >> Assessments” page. This is critically important for you to understand the elements and requirements of the CW.
• This assessment is in Amber category for the use of AI, meaning that you can use AI for assistance.

Submission Instructions:

Submission arrangement online via AULA:

Submit before 18:00, late work will receive a mark of zero.
File types and method of recording: Submit a Single Word file (preferred) or a pdf file. Mark and Feedback date: 25/12/2025
Mark and Feedback method: given on each script.

Your final submission will be scientific outputs of two folds:

1.    A “scientific paper” of up to 6 A4 pages based on the experience and results gained during the project work.
2.    A viva video recording your introduction to the dataset preparation, data wrangling, model training and testing, demonstration of running the pipeline (especially producing prediction outputs from your model), and model evaluation etc.

You are encouraged to target a certain conference or journal and submit the proposed paper to it. Submission guidelines can be found on the conference or journal web page you choose to submit to.

List of reputed conferences and journals:
1-    IJCNN Conference
2-    NeurIPS Conference
3-    International Conference of Machine Learning
4-    Machine Learning Journal
5-    Neural Networks Journal
6-    Others (please let us know) PAPER STRUCTURE

•    Abstract
•    Introduction (where you introduce the problem along a short literature review of related work; if the literature review is longer, it is recommended to be a section on its own, which would be better)
•    Problem and Data set(s) description (where you describe in detail the problem you want to solve and its significance)
•    Methods (where you shortly describe the machine learning methods and/or other methods employed to solve the problem)
•    Experimental setup (including data pre-processing, feature selection and extraction, classification/clustering parameters)
•    Results
•    Discussion and Conclusions
•    Social, ethical, legal and professional considerations
•    References

Assessed Module Learning Outcomes

The Learning Outcomes for this module align to the marking criteria which is provided above. Ensure you understand the marking criteria to ensure successful achievement of the assessment task. The following module learning outcomes (highlighted in bold) are assessed in this task:

On successful completion of this module a student should be able to:

1. Understand and critically apply fundamental concepts of probability, statistical models, and inference techniques to real-world data analysis.
2. Implement and critically apply both supervised and unsupervised machine learning algorithms, effectively utilising techniques such as classification, regression, clustering, and dimensionality reduction.
3. Critically evaluate and interpret model performance using appropriate metrics and apply validation techniques such as cross-validation and regularisation to avoid overfitting and underfitting.
4. Critically analyse statistical and machine learning models, taking into account issues of bias, fairness, interpretability, and under/over fitting.

Administration of Assessment

Module Leader Name: Dr Omid Chatrabgoun Module Leader Email: ad8337@coventry.ac.uk Assignment Category: Written
Attempt Type: Standard
Component Code: CW

General marking guidelines