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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
Term First Term 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

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

Exam

Assessment Type Summative Weighting 70
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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
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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
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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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