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Understand the main concepts of the modern artificial intelligence (AI) and machine learning. Critically evaluate and practice a range of machine learning algorithms, tools and frameworks for developing AI solutions.

Both cheating and plagiarism are totally unacceptable and the University maintains a strict policy against them. It is YOUR responsibility to be aware of this policy and to act accordingly.

The basic principles are:

  • Don’t pass off anyone else’s work as your own, including work from “essay banks”. This is plagiarism and is viewed extremely seriously by the University.
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You should be aware that coursework may be submitted to an electronic detection system in order to help ascertain if any plagiarized material is present. You may check your own work prior to submission using Turnitin. If you have queries about what constitutes plagiarism, please speak to your module tutor or the Centre.

Electronic Submission of Work.

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Learning Outcomes to be Assessed:

  • Understand the main concepts of the modern artificial intelligence (AI) and machine learning.
  • Critically evaluate and practice a range of machine learning algorithms, tools and frameworks for developing AI solutions.
  • Apply the learned algorithms, tools and frameworks to solve real-life problems.
  • Demonstrate skills in formulating research problems and writing technical reports.

Rationale:

This coursework is most suited for assessing the learning outcomes of the module providing the practical nature of the AI field. The area is growing fast and the interest in machine learning solutions constantly increases. Learning to formulate and solving practical and research-oriented data-driven projects will ensure your continuing employability through development of analytical soft skills. The group nature of the coursework allows learning working in teams.

Description:

For this re-assessment coursework you are required to find a dataset, formulate a problem you want to address with the dataset (e.g. predict whether a mushroom is poisonous or not based on its characteristics), build and evaluate a machine learning model that would address the problem, and draw conclusions and recommendations based on your findings. The submission should include your report, dataset (plus any number of sets representing pre-processing stages) and Python scripts with comments, all included in one zip-file. Your work should be original and produced by you. Copying whole tutorials, scripts or images from other sources is not allowed. Any material you borrow from other sources to build on should be clearly referenced (use comments to reference in Python scripts), otherwise it will be treated as plagiarism, which may lead to investigation and subsequent action.

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