Documentary evidence (including source code listing) should be provided as appropriate within your report.
2025-01-03 16:39:54
School of Computing and Engineering
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Title
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Assessment
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Module
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Machine Learning
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Module Code
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CP60057E, CP6CS57E, CP6SA57E, CP6HA57E
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Type of assessment: Coursework
Weighting: 100%
Assessor: Tutor
Each assignment has a specific weighting, and its own criteria.
- Assessment Item-1: Logbook on practical sessions
- 100% Weighting
- Deadline: look at Blackboard page
You must achieve an overall mark of at least 40% to pass the module.
Feedback will be given within 15 working days.
1. Details of Assessment
This assessment is an individually assessed component. Please make sure you have a clear understanding of the grading principles for this component. If you are unsure about any aspect of this assessment component, please seek the advice of a member of the delivery team.
The task involves data analysis, and discussion of methods and results, using Python. For your report, you must submit a single PDF file that contains all answers, including any text needed to describe your results, the example code snippets used to answer each problem, any figures that were generated, and scans of any (clearly readable) work on paper that you want the graders to consider. It is important that you include enough detail that we know how you solved each problem.
You will need to ensure that your logbook is uploaded as a single PDF document. Note: Documentary evidence (including source code listing) should be provided as appropriate within your report. When submitting, name your PDF file using this format: StudentID_LastName_ FirstName (for example: 12345678_Zolgharni_Massoud). You must attend the lectures for further details, guidance and clarifications regarding these instructions.
Assignments are to be submitted to Turnitin. You will find a link to the Turnitin Assignments from the Assessments area of the Blackboard course menu. Turnitin generates an Originality Report, and you are encouraged to make use of this facility as a support tool to help you ensure the source material in your assignment is correctly referenced before final submission.
At the due date and time, no further submissions or changes are possible. Whatever is in the Turnitin inbox at this time will be regarded as your final submission.
Extensions will only be granted in exceptional circumstances. Extensions must be agreed before the deadline. Extensions will be for 10 days or less for the course work. Documentary evidence will be required. Submissions for course work up to one week late with no extension will be marked with a maximum mark of 40%.
Full details of the University Assessment Regulations can be found at: http://www.uwl.ac.uk/students/current_students/Student_handbook.jsp
For guidance on online submission of assignments, including how to submit and how to access online feedback, please refer to the UWL Blackboard student-help pages at:
http://www.uwl.ac.uk/blackboardhelp
Marking grid:
Criteria
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Issues
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Mark
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Marking breakdown where appropriate
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Learning Outcomes
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Report
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Critique the theory of machine learning
Apply a range of machine learning techniques to solve practical problems
Critically evaluate and Interpret the results
Quality of the report
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100
(total)
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30
30
30
10
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LO1-LO4
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Grade descriptors
In addition to the assessment criteria above the following table may assist you in understanding how we arrive at your final mark. Indeed your final mark should agree with the following grade descriptors, but note that the assessment criteria are the main means of assessment.
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