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Current postgraduate taught students

COMP61021: Modelling and visualization of high-dimensional data (2011-2012)

This is an archived syllabus from 2011-2012

Modelling and visualization of high-dimensional data
Level: 6
Credit rating: 15
Pre-requisites: COMP61011: Machine Learning and Data Mining
Co-requisites: No Co-requisites
Lectures: three hours per week (5 weeks)
Labs: three hours per week (5 weeks)
Lecturers: Ke Chen
Course lecturer: Ke Chen

Additional staff: view all staff
Sem 1 P2 Lecture 2.15 Thu 09:00 - 17:00 -
Assessment Breakdown
Exam: 50%
Coursework: 50%
Lab: 0%

Themes to which this unit belongs
  • Learning from Data


A major component of machine learning and data mining is dealing with the high dimensional data that arises. Typical examples include pixels from an image (millions of dimensions), medical data bases (perhaps hundreds of dimensions, often with missing values), video clips and speech signals (time series data of very high dimensions), and gene expression data (expression values of many thousands of genes). Dealing with high dimensional data is a key challenge for modern computer science.
In this course we will consider how to develop appropriate algorithms for modelling and visualizing these high dimensional data sets and gain insights into these algorithms from theoretical and empirical perspectives. We will demonstrate how these algorithms can be applied through the use of examples and real world problems. We will cover approaches from machine learning, statistics and neural computation.


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.

Programme outcomeUnit learning outcomesAssessment
A1 A2 A3 B1 G1Have knowledge and understanding of the principle approaches to dimensionality reduction of high dimensional data as outlined in the syllabus below.
  • Examination
A1 A3 G1 G2Develop their essential mathematical knowledge to understand the theoretical underpinnings of dimension reduction techniques.
  • Examination
B1 B3 C1 G3Be able to apply dimensionality reduction algorithms to real datasets, evaluate their performance and appreciate the practical issues involved.
  • Presentation
  • Lab assessment
B3 C4 D2 G4Be able to appraise the methods of a systematic review, and understand their importance and limitations.
  • Lab assessment
  • Presentation


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)

Reading List

Title: Introduction to machine learning (3rd edition)
Author: Alpaydin, Ethem
ISBN: 9780262028189
Publisher: MIT Press
Edition: 3rd
Year: 2014

Title: Pattern recognition and machine learning
Author: Bishop, Christopher M.
ISBN: 9780387310732
Publisher: Springer
Year: 2006

Title: Neural networks and learning machines (3rd edition)
Author: Haykin, Simon
ISBN: 9780131293762
Publisher: Pearson
Edition: 3rd
Year: 2008