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JC3509: MACHINE LEARNING (2023-2024)

Last modified: 23 Jul 2024 10:44


Course Overview

This course provides an introduction to machine learning and data mining. Students will learn how to analyse complex datasets by applying data pre-processing, exploration, clustering and classification, time-series analysis, neural networks, and many other techniques. This course is particularly suitable for those who are interested in working as data analysts or data scientists in the future.

Course Details

Study Type Undergraduate Level 3
Term Second Term Credit Points 15 credits (7.5 ECTS credits)
Campus Offshore Sustained Study No
Co-ordinators
  • Dr Tryphon Lambrou

What courses & programmes must have been taken before this course?

  • Any Undergraduate Programme (Studied)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

This course introduces the student to machine learning techniques, as the means for computer systems to extract useful information out of data. Topics include:

  • Obtaining, preparing, managing, and presenting data
  • Supervised learning, classification, regression
  • Unsupervised learning, clustering
  • Decision-tree learning
  • Neural networks and deep learning
  • Case-studies and applications

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 30 August 2024 for 1st term courses and 20 December 2024 for 2nd term courses.

Summative Assessments

Assignment

Assessment Type Summative Weighting 30
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
ProceduralAnalyseAbility to identify, prepare, and manage appropriate datasets for analysis.
ProceduralApplyAbility to appropriately present the results of data analysis.
ProceduralEvaluateAbility to analyse the results of data analyses, and to evaluate the performance of analytic techniques in context.
ProceduralEvaluateKnowledge and understanding of analytic techniques, and ability to appropriately apply them in context, making correct judgements about how this needs to be done.

Exam

Assessment Type Summative Weighting 70
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
ProceduralAnalyseAbility to identify, prepare, and manage appropriate datasets for analysis.
ProceduralApplyAbility to appropriately present the results of data analysis.
ProceduralEvaluateAbility to analyse the results of data analyses, and to evaluate the performance of analytic techniques in context.
ProceduralEvaluateKnowledge and understanding of analytic techniques, and ability to appropriately apply them in context, making correct judgements about how this needs to be done.

Formative Assessment

There are no assessments for this course.

Resit Assessments

Exam

Assessment Type Summative Weighting 100
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ProceduralApplyAbility to appropriately present the results of data analysis.
ProceduralEvaluateKnowledge and understanding of analytic techniques, and ability to appropriately apply them in context, making correct judgements about how this needs to be done.
ProceduralEvaluateAbility to analyse the results of data analyses, and to evaluate the performance of analytic techniques in context.
ProceduralAnalyseAbility to identify, prepare, and manage appropriate datasets for analysis.

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