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PX5509: MACHINE LEARNING (2020-2021)

Last modified: 05 Aug 2021 13:04


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 Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr F. J. Perez-reche

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

  • Any Postgraduate Programme
  • Either Master Of Science In Health Data Science or Master Of Science In Data Science

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

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 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

1 assessed practical (50%)

1 MCQ test via Blackboard (50%)

Resit (for students taking the course in AY20/21)

Resit of any failed element

Formative Assessment

There are no assessments for this course.

Course Learning Outcomes

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

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