MSc ACS: Computer Security

Year of entry: 2020

Course unit details:
Modelling and Visualisation of High-Dimensional Data

Unit code COMP61021
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by Department of Computer Science
Available as a free choice unit? Yes

Overview

This is a research-oriented advanced machine learning course that is suitable for MSc students in CS who are interested in machine learning, data mining and their applications to intelligent systems. It would be particularly helpful for those who want to pursue PhD studies in a related discipline.

Pre/co-requisites

Unit title Unit code Requirement type Description
Foundations of Machine Learning COMP61011 Pre-Requisite Compulsory

Aims

This course unit aims to introduce students to state-of-the-art approaches to dealing with high dimensional data based on dimensionality reduction and provides experience of research such as literature review and appraising research papers in modelling and visualization of high dimensional data. In particular, transferable knowledge/skills, essential to original researches, are highlighted in this course unit.

Learning outcomes

  • describe the curse of dimensionality and its implication in different learning paradigms including supervised and unsupervised learning

  • understand the general motivation and main ideas behind dimension reduction techniques 

  • understand the advantages and the disadvantages of the learning algorithms studied in the course unit and decide which is appropriate for a particular application

  • derive the principal component analysis (PCA) and the linear discriminative analysis (LDA) algorithms

  • apply the learning algorithms studied in the course unit to simple data sets for dimension-reduction related applications

  • implement PCA and Kohonen’s self-organised maps as well as apply them to  real-world datasets for data modelling and visualisation

  • evaluate the performance of the learning algorithms studied in the course unit and  whether a learning algorithm is appropriate for a particular problem

  • understand and appreciate main ideas underlying a state-of-the-art dimension reduction algorithm

Syllabus

  • Introduction/Background
  • Mathematics Basics
  • Principal component analysis (PCA)
  • Linear discriminative analysis (LDA)
  • Self-organising map (SOM)
  • Multi-dimensional scaling (MDS)
  • Isometric feature mapping (ISOMAP)
  • Locally linear embedding (LLE)

Teaching and learning methods

Lectures

three hours per week (5 weeks)

Laboratories

three hours per week (5 weeks)

Employability skills

Analytical skills
Group/team working
Oral communication
Problem solving
Research
Written communication

Assessment methods

Method Weight
Written exam 50%
Written assignment (inc essay) 50%

Feedback methods

In general, feedback is available for the assessed work.

For coursework, the feedback to individuals will be offered.

For exam, the general feedback to the whole class will be given in writing.

Recommended reading

COMP61021 reading list can be found on the School of Computer Science website for current students.

Study hours

Independent study hours
Independent study 150

Teaching staff

Staff member Role
Ke Chen Unit coordinator

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.

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