Machine Learning and Data Analytics

MachineLearning and Data Analytics

University of Sunderland, UK

Faculty of Technology, Department of Computer Science

1

UNIVERSITY OF SUNDERLAND

FACULTY OF TECHNOLOGY

DEPARTMENT OF COMPUTER SCIENCE

MODULE CODE: CETM47

MODULE TITLE:

Machine Learning and Data Analytics

MODULE ASSESSOR: Dr Valentina Plekhanova

ASSESSMENT: One of two

TITLE OF ASSESSMENT: Assignment 1 – Research

PLEASE READ ALL INSTRUCTIONS AND INFORMATION CAREFULLY.

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 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 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).

Submission Date and Time Will be available on the Module CANVAS

Submission Location Use Module CANVAS and the link “Assignment

Submission” within Assessment Unit (see left-side column)
to submit your assignment document.

Machine Learning and Data Analytics
University of Sunderland, UK
Faculty of Technology, Department of Computer Science

2

The assignment is based on individual work by each student.

The module will be assessed by two coursework assignments. CETM47 Assignment

One covers learning outcomes 1, 2, 3, 4, and 5 of the module. There will be a report

containing a research component where students will be required to research and

present their critical evaluation of a specific area of Machine Learning and Data

Mining tools/techniques. The submission requirements consists of a report of 3000

words maximum plus appendices containing, e.g. references, screenshots, etc. If

students exceed the word limit then the penalty as specified in “Guidance for Students

on the Penalty for Exceeding the Limit for Assessed Work” shall apply.

You need to choose a practical problem and/or research problem. You will write a

report that addresses your chosen problem.

You are free to structure/organise your report in whatever way best explains your

work. However, you must ensure that it includes the following elements that you are

expected to hand in:

1. A description of the practical problem and/or relevant research problem.
2. Explanations with clear arguments why selected problem is important.
3. Research questions and/or hypothesis.
4. Assessment of needs for use of problem relevant Machine Learning and/or

Data Mining technique(s).

5. Critical considerations of ethical, legal and professional issues

6. Presentation of a formal statement of the given problem.
7. A critical evaluation of problem relevant Machine Learning and/or Data

Mining tools/techniques.

8. Define the data that is required to address the given problem. A selection of
Data Collection Method(s). References to data sources. Problems with

Data.

9. Identification of critical/key aspects that could affect application of the chosen
technique(s).

10. Criteria or criterion for result quality measurement/assessment and relevant
measures should be presented and critically discussed.

11. Description and explanation of support which is needed for use of the
proposed Machine Learning and/or Data Mining technique(s).

12. Discussion of the level of success achieved and any enhancements which

would improve the work.

You need to consider the following aspects of your report:

Description of the practical problem and/or relevant research problem; problem

statement and analysis

You should present a clear outline of the problem or issue that you will address in

your work, including the following key aspects:

 Who has responsibility for the problem?

 What has already been done to try to solve it?

 What will happen if the problem is not solved?

https://my.sunderland.ac.uk/download/attachments/105484811/Guidance%20for%20students%20on%20the%20penalty%20for%20exceeding%20the%20limit%20for%20assessed%20work%20v2 ?version=4&modificationDate=1504882046000&api=v2

https://my.sunderland.ac.uk/download/attachments/105484811/Guidance%20for%20students%20on%20the%20penalty%20for%20exceeding%20the%20limit%20for%20assessed%20work%20v2 ?version=4&modificationDate=1504882046000&api=v2

Machine Learning and Data Analytics
University of Sunderland, UK
Faculty of Technology, Department of Computer Science

3

Define research questions and/or hypothesis.

A critical evaluation of problem relevant Machine Learning and/or Data Mining

tools/techniques.
Establish the problem relevant issues/theory and what you expect to find.

You need to position a focus of your work, critically evaluate relevant work (theory),

how the current problem relevant Machine Learning / Data Mining tools were

used/applied, and identify if there are any missing aspects (gap). You should critically

evaluate at least four problem relevant Machine Learning / Data Mining tools from

relevant academic journal articles, conference papers or other peer-reviewed material.

Key points and discussions should be supported by your arguments. Also you should

consider if authors of these papers present any ethical, legal and professional aspects

and if there are any relevant ones that should be addressed.

Identification and discussion of relevant professional, ethical, social and legal issues

in research applicable to your programme of study.

It should include a critique of how professional, ethical, social and legal considerations

are addressed if applicable.

Description and explanation of support for use of the proposed Machine Learning and

Data Mining technique(s), e.g. data quality, performance measures, ethical issues, etc.

Also you could provide conclusions and suggestions on incorporating your work

findings into practices.

Discussion of the level of success achieved and any enhancements which would

improve the work. A concise summary of the arguments presented in the papers and a

clear reflection on what you have learnt from your work and how it could be applied

to your future work, e.g. research, MSc project.

You are expected to hand in a report. 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.

Use the link “Assignment” within Assessments Unit (see left-side menu column) to
submit your assignment document – before the deadline.

Marking Scheme

Part One Marks

1. A description of the practical problem and/or relevant research problem.
2. Explanations with clear arguments why selected problem is important.
3. Research questions and/or hypothesis.
4. Assessment of needs for use of problem relevant Machine Learning

30

Machine Learning and Data Analytics
University of Sunderland, UK
Faculty of Technology, Department of Computer Science

4

and/or Data Mining technique(s).

5. Critical considerations of professional, ethical, social and legal issues.

Part Two

6. Presentation of a formal statement of the given problem.
7. A critical evaluation of problem relevant Machine Learning and/or Data
Mining tools/techniques.

30

Part Three

8. Define the data that is required to address the given problem. A selection
of Data Collection Method(s). References to data sources. Problems with

Data.

9. Identification of critical/key aspects that should be addressed and could
affect application of the chosen technique(s).

10. Criteria or criterion for result quality measurement/assessment and

relevant measures should be presented and critically discussed.

30

Part Four

11. Description and explanation of support for use of the proposed Machine
Learning and Data Mining technique(s).

12. Discussion of the level of success achieved and any enhancements which
would improve the work.

10

Total: 100