CETM47: Machine Learning and Data Analytics - IT Assignment Help

Assignment Task:

This assignment contributes 50% to your final module mark. Please ensure that you retain a duplicate of your assignment. We are required to send samples of student work to the external examiners for moderation purposes. It will also safeguard in the unlikely event of your work going astray. All assignment papers are required to be submitted into the TurnItIn system to check for plagiarism. Use Module CANVAS and the link "Assignment Submission" within the Assessment Unit (see left-side column) to submit your assignment document.

THE FOLLOWING LEARNING OUTCOMES WILL BE ASSESSED:
Knowledge of:
1. A critical understanding of trends, tools, and current developments in the areas of Machine Learning, Data Mining and Data Analytics
2. A critical understanding of Machine Learning, Data Mining and Data Analytics tools
3. Understanding of the professional, ethical, social and legal considerations involved in Data Mining and Data Analytics

And the ability to:
4. To critically assess, choose and apply the appropriate Machine Learning, Data Mining and Data Analytics formalisms and tools to practical problems
5. To identify and assess data for the use of Data Mining and Data Analytics tools
6. To define, explain and interpret the results obtained from the practical application of Machine Learning, Data Mining and Data Analytics tools.

IMPORTANT INFORMATION
You are required to submit your work within the bounds of the University Infringement of Assessment Regulations (see your Programme Guide). Plagiarism, paraphrasing and downloading large amounts of information from external sources, will not be tolerated and will be dealt with severely. Although you should make full use of any source material, which would normally be an occasional sentence and/or paragraph (referenced) followed by your own critical analysis/evaluation. You will receive no marks for work that is not your own. Your work may be subject to checks for originality which can include the use of an electronic plagiarism detection service.

Where you are asked to submit an individual piece of work, the work must be entirely your own. The safety of your assessments is your responsibility. You must not permit another student access to your work.

Where referencing is required, unless otherwise stated, the Harvard referencing system must be used (see your Programme Guide).

You are expected to hand in:
A report should contain the following:

• Description of the problem.
• Critical evaluation and selection of the current problem relevant Machine Learning/Data Analytics tools.
• Data collection. References to data sources. Problems with data.
• Critical considerations of professional, ethical, social and legal issues
• Presentation of a formal statement of the given problem/task.
• Application of the proposed Machine Learning / Data Analytics tools to the chosen problem.
• Assessment of the proposed Machine Learning / Data Analytics technique(s) performance. Use of performance measures.
• Critical evaluation of application and results of the chosen technique (e.g. compare/measure of model performance, etc.)
• Description and explanation of support which is needed for use of the proposed Machine Learning / Data Analytics tools.
• Discussion of the level of success achieved and any enhancements which would improve the work.

You could use appendixes to introduce details of relevant work, screenshots, copies of papers used for critical evaluation and collected data. Excluding the appendixes, title page and list of references, assignment work should be 3000 words maximum.

There are a total of 100 possible marks in this assignment. Your work shall be graded for originality as well as for accuracy.

Marking Scheme

Part One
1. A description of the practical problem and/or relevant research problem.
2. Explanations with clear arguments why the selected problem is important.
3. Research questions and/or hypothesis.
4. Critical considerations of professional, ethical, social and legal issues.

Part Two
5. Presentation of a formal statement of the given problem.
6. A critical evaluation of problem relevant Machine Learning / Data Analytics tools/techniques.

Part Three
7. Data collection. References to data sources. Problems with Data.
8. Criteria or criterion for result quality measurement/assessment and relevant measures should be presented and discussed.
9. Application of the proposed Machine Learning / Data Analytics tools to the chosen problem.
10. Assessment of the proposed Machine Learning / Data Analytics technique(s) performance. Use of performance measures.
11. Critical evaluation of application and results of the chosen technique(s) (e.g. compare/measure of model performance, etc.).

Part Four
12. Description and explanation of support for use of the proposed Machine Learning / Data Analytics technique(s).
13. Discussion of the level of success achieved and any enhancements which would improve the work.

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