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PX5009: MACHINE LEARNING (2019-2020)

Last modified: 25 Sep 2019 09:58


Course Overview

In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.

Great emphasis will be given to the actual application of all these methods to small and large data sets.

Course Details

Study Type Postgraduate Level 5
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Francisco Perez-Reche

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

  • Master Of Science In Data Science
  • Any Postgraduate Programme

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

In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.

The course is very practical and great emphasis will be on the actual application of all these methods to small and large data sets.

First, we will use high level functions that perform these analyses in an automated way and we will focus on data preparation and interpretation of the results.

As the course progresses, we will move to more advanced techniques and construct for example neural networks from scratch. We will learn how to perform network surgery to benefit from pertained networks and achieve maximal accuracy and efficiency in our training and for the predictions.


Contact Teaching Time

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

Teaching Breakdown

  • 3 Lectures during University weeks 13 - 15
  • 3 Practicals during University weeks 13 - 15

More Information about Week Numbers


In light of Covid-19 and the move to blended learning delivery the assessment information advertised for courses may be subject to change. All updates for first-half session courses will be actioned no later than 1700 (GMT) on 18 September 2020. All updates for second half-session courses will be actioned in advance of second half-session teaching starting. Please check back regularly for updates.

Summative Assessments

Exam

Assessment Type Summative Weighting 70
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

Take Home Exam

Assessment Type Summative Weighting 20
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
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Tutorial/Seminar Participation

Assessment Type Summative Weighting 10
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

Formative Assessment

There are no assessments for this course.

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ReflectionUnderstandUnderstanding difference between stochastic and deterministic processes
ProceduralApplyKnowledge of modelling techniques
ReflectionApplyUnderstanding computation processes

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