TEESSIDE UNIVERSITY School of Computing, Engineering and Digital Technologies Module Title: Machine Learning Module Code: CIS4035-N Assignment Title: Machine Learning Application and Report
Problems in machine learning vary from domain to another. In this coursework, you will select a dataset related to a real-world problem that best suits your area of interest. There are abundant of websites that provide publicly available datasets. A categorised list of datasets from GitHub can be found at https://github.com/caesar0301/awesome-public-datasets. The UCI Machine Learning Repository at https://archive.ics.uci.edu/ml/index.php is another longstanding source of benchmark datasets for data mining and machine learning research. Kaggle https://www.kaggle.com/datasets has interesting real-world problems and datasets.
You can select a dataset from the above sources, or another one that is available online. The dataset should be publicly available. The chosen dataset should have a minimum of 1,000 instances (rows) and a minimum of 5 attributes (columns). You have to complete the following stages in this assignment:
Element 1 will assess learning outcomes LO 2, 3, 4, 5 and 6
You need to demonstrate your code and results in the practical sessions in the last week (w/c 27th April 2020).
The code and experiments will be assessed on
Element 2 will assess learning outcomes LO 1, 2 and 7
The hand in is electronically via Blackboard, all deliverables shall be labelled with project name, your student name and university number.
The report will be assessed on:
The report could broadly include the following sections:
These are generic section titles, which you may adapt appropriately to the application/problem that is investigated. You may include sections describing modifications of algorithms or developments that are novel and specific to your work.
Grade |
SOURCE CODE DOCUMENTATION AND DEMO |
Excellent 70% and above |
Clear evidence of running the experiments with code that is excellently organised and commented. Machine learning algorithms selected are appropriate for the given task Excellent quality of software architecture and implementation Excellent quantitative performance of application Deep understanding shown. |
Very Good 60% - 69% |
Very good evidence of running the experiments with code that is well organised and commented. Machine learning algorithms selected are appropriate for the given task Very good quality of software architecture and implementation Very good quantitative performance of application Very good understanding. |
Satisfactory 50% - 59% |
Satisfactory evidence of running the experiments with code that is organised and commented. Machine learning algorithms selected are appropriate for the given task Satisfactory quality of software architecture and implementation Satisfactory quantitative performance of application Satisfactory understanding. |
Fail Less than 50% |
Little evidence of running the experiments with code that is not well organised and commented. Machine learning algorithms selected are not appropriate for the given task Poor quality of software architecture and implementation Poor quantitative performance of application Poor understanding. |
NS NON- SUBMISSION |
N/A |
Grade |
ACADEMIC QUALITY OF THE PAPER - 50% |
Excellent 70% and above |
Excellent technical quality (rigour of the experiments, data preparation, justification and correct application of the selected algorithms and suitability of the selection). Produced and demonstrated a comprehensive, high quality solution to the problem. Sufficient information for the reader is provided to reproduce the results. Outstanding evidence of systematic review using multiple high quality academic sources. Logical, clear development of narrative. High quality references and citations. Outstanding evaluation and discussion of the significance of the results (Why the results are important? How does the paper advance the state of the art? How would the results be useful to other researchers or practioners? Is this a “real” problem or a small “toy” problem?) |
Legal, social, ethical, security and professional issues fully considered. A paper, which could be, with minor modifications, suitable for a publication – or form the basis for a postgraduate project. There is some element of a novel approach to the problem or novel use of techniques. |
|
Very Good 60% - 69% |
Very good technical quality. Produced and demonstrated very good quality solution to the problem. Sufficient information for the reader is provided to reproduce the results. Very good evidence of systematic review using multiple high quality academic sources. Logical, clear development of narrative. Appropriate references and citations. Very good evaluation and discussion of the significance of the results. Legal, social, ethical, security and professional issues fully considered. |
Satisfactory 50% - 59% |
Satisfactory technical quality. Produced and demonstrated good quality solution to the problem. Good evidence of reviewing multiple academic sources. Some references and citations. Good evaluation and discussion of the significance of the results. Legal, social, ethical, security and professional issues fully considered. |
Fail Below 50% |
Not adequate technical quality. Produced and demonstrated a solution to the problem, which is flawed, despite some effort. Poor evidence of reviewing academic sources. Little evaluation and discussion of the results. Little consideration of legal, social, ethical, security and professional issues. Narrative difficult to follow. Poor quality of references and citations. |
NS NON- SUBMISSION |
N/A |
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