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| Category | Assignment | Subject | Computer Science |
|---|---|---|---|
| University | Coventry University | Module Title | 7044SCN Machine Learning and Statistical Modelling |
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
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.\
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
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:
Module Leader Name: Dr Omid Chatrabgoun Module Leader Email: ad8337@coventry.ac.uk Assignment Category: Written
Attempt Type: Standard
Component Code: CW
| Assessment Marking Criteria: | Mark |
|
Technical quality 1) Rigour and extent of the experiments. 2) Correct application of the selected algorithms and suitability of the methods. 3) Data preparation - technical quality. 4) Evidence of running the experiments provided in appendices and the informal VIVA video. Is there sufficient information for the reader to reproduce the results? |
10% 10%
10% 10% |
|
Evaluation 5) Evaluation and discussion of the results. Why the results are important? How would the results be useful to other researchers or practitioners? 6) Is this a “real” problem or a small “toy” problem? How does the paper advance the state of the art? |
20%
5% |
| 7) Social, ethical, legal and professional considerations related to the problem in question. (99% lost this easy-to-get mark by getting 0) | 5% |
|
Clarity of the writing: 8) Is th:e language used in the paper good? 9) References and general presentation; Are results clearly presented, with appropriate visualisations? |
10% 10%
|
|
Originality: 10) Is there some original approach to the problem, original use of techniques? Is there any (and how much) difference from previous contributions? |
10% |
|
Mark band |
Outcome |
Guidelines |
|
90-100% Distinction |
Meets learning outcomes |
Distinction - Exceptional work with very high degree of rigour, creativity and critical/analytic skills. Mastery of knowledge and subject-specific theories with originality and autonomy. Demonstrates exceptional ability to analyse and apply concepts within the complexities and uncertainties of the subject/discipline. Innovative research with exceptional ability in the utilisation of research methodologies. Demonstrates, creativity, originality and outstanding problem-solving skills. Work completed with very high degree of accuracy, proficiency and autonomy. Exceptional communication and expression demonstrated throughout. Student evidences the full range of technical and/or artistic skills. Work pushes the boundaries of the discipline and may be strongly considered for external publication/dissemination/presentation. |
|
80-89% Distinction |
Distinction - Outstanding work with high degree of rigour, creativity and critical/analytic skills. Near mastery of knowledge and subject-specific theories with originality and autonomy. Demonstrates outstanding ability to analyse and apply concepts within the complexities and uncertainties of the subject/discipline. Innovative research with outstanding ability in the utilisation of research methodologies. Work consistently demonstrates creativity, originality and outstanding problem-solving skills. Work completed with high degree of accuracy, proficiency and autonomy. Outstanding communication and expression demonstrated throughout. Student demonstrates a very wide range of technical and/or artistic skills. With some amendments, the work may be considered for external publication/dissemination/presentation |
|
|
70-79% Distinction |
Distinction - Excellent work undertaken with rigour, creativity and critical/analytic skills. Excellent degree of knowledge and subject-specific theories with originality and autonomy demonstrated. The work exhibits excellent ability to analyse and apply concepts within the complexities and uncertainties of the subject/discipline. Innovative research with excellent ability in the utilisation of research methodologies. Work demonstrates creativity, originality and excellent problem-solving skills. Work completed with very consistent levels of accuracy, proficiency and autonomy. Excellent communication and expression demonstrated throughout. Student demonstrates a very wide range of technical and/or artistic skills. |
|
60-69% Merit |
|
Merit - Very good work often undertaken with rigour, creativity and critical/analytic skills. Very good degree of knowledge and subject-specific theories with some originality and autonomy demonstrated. The work often exhibits the ability to fully analyse and apply concepts within the complexities and uncertainties of the subject/discipline. Very good research evidence and shows very good ability in the utilisation of research methodologies. Work demonstrates creativity, originality and problem-solving skills. Work completed with very consistent levels of accuracy, proficiency and autonomy. Very good communication and expression demonstrated throughout. Student demonstrates a wide range of technical and/or artistic skills. |
|
50-59% Pass |
Pass - Good work undertaken with some creativity and critical/analytic skills. Demonstrates knowledge and subject- specific theories with some originality and autonomy demonstrated. The work exhibits the ability to analyse and apply concepts within the complexities and uncertainties of the subject/discipline. Good research and shows some ability in the utilisation of research methodologies. Work demonstrates problem-solving skills and is completed with some level of accuracy, proficiency and autonomy. Satisfactory communication and expression demonstrated throughout. Student demonstrates some of the technical and/or artistic skills. |
|
|
40-49% Pass |
Pass - Assessment demonstrates some advanced knowledge and understanding of the subject informed by current practice, scholarship and research. Work may be incomplete with some irrelevant material present. Sometimes demonstrates the ability to analyse and apply concepts within the complexities and uncertainties of the subject/discipline. Acceptable research with evidence of basic ability in the utilisation of research methodologies. Demonstrates some originality, creativity and problem-solving skills but often with inconsistencies. Expression and presentation sufficient for accuracy and proficiency. Sufficient communication and expression with professional skill set. Student demonstrates some technical and/or artistic skills. |
|
|
30-39% Fail |
Fails to achieve learning outcomes |
Fail - Very limited understanding of relevant theories, concepts and issues with deficiencies in rigour and analysis. Some relevant material may be present but be informed from very limited sources. Fundamental errors and some misunderstanding likely to be present. Demonstrates limited ability to analyse and apply concepts within the complexities and uncertainties of the subject/discipline. Limited research scope and ability in the utilisation of research methodologies. Limited originality, creativity, and struggles with problem-solving skills. Expression and presentation insufficient for accuracy and proficiency. Insufficient communication and expression and with deficiencies in professional skill set. Student demonstrates deficiencies in the range of technical and/or artistic skills. |
|
20-29% Fail - |
|
Fail - Clear failure demonstrating little understanding of relevant theories, concepts, issues and only a vague knowledge of the area. Little relevant material may be present and informed from very limited sources. Serious and fundamental errors and virtually no evidence of relevant research. Fundamental errors and misunderstandings likely to be present. Little or no research with no evidence of utilisation of research methodologies. No originality, creativity, and struggles with problem-solving skills. Expression and presentation insufficient for accuracy and proficiency. Insufficient communication and expression and with serious deficiencies in professional skill set. Student has clear deficiencies in range of technical and/or artistic skills. |
|
0-19% Fail |
Fail - Clear failure demonstrating no understanding of relevant theories, concepts, issues and no understanding of area. Little or no relevant material may be present and informed from minimal sources. No evidence of ability in the utilisation of research methodologies. No evidence of originality, creativity, and problem-solving skills. Expression and presentation deficient for accuracy and proficiency. Insufficient communication and expression and with deficiencies in professional skill set. Student has clear deficiencies in range of technical and/or artistic skills. |
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