Advanced Computer Science: Multi-Core Computing [MSc]

Modelling and Visualisation of High-Dimensional Data


Unit code: COMP61021
Credit Rating: 15
Unit level: Level 6
Teaching period(s): Semester 1
Offered by School of Computer Science
Available as a free choice unit?: Y

Requisites

Prerequisite

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.

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.

Teaching and learning methods

Lectures

three hours per week (5 weeks)

Laboratories

three hours per week (5 weeks)

Learning outcomes

Learning outcomes are detailed on the COMP61021 course unit syllabus page on the School of Computer Science's website for current students.

Employability skills

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

Assessment methods

  • Written exam - 50%
  • Written assignment (inc essay) - 50%

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)

Recommended reading

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

Feedback methods

In general, feedback is available for the assessed work.

For coursework, the feedback to individuals will be offered during on-site marking in the lab.

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

Study hours

  • Independent study hours - 64 hours

Teaching staff

Ke Chen - Unit coordinator

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