This is an archived syllabus from 2019-2020

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COMP61021 Modelling and Visualisation of High-Dimensional Data

Level 6
Credits: 15
Enrolled students: 128

Course leader: Ke Chen

Additional staff: view all staff

Requisites

• Pre-Requisite (Compulsory): COMP61011

Assessment methods

• 50% Written exam
• 50% Coursework

Timetable

Semester	Event	Location	Day	Time	Group
Sem 1 w7-10	Lab	1.8	Wed	14:00 - 17:00	-
Sem 1 w7-10	Lab	2.25 (A+B)	Wed	14:00 - 17:00	-
Sem 1 w7	Lecture	Schuster RUTHERFORD TH	Wed	09:00 - 12:00	-
Sem 1 w8	Lecture	Simon TH A	Wed	09:00 - 11:00	-
Sem 1 w9	Lecture	Simon 3.44A	Wed	09:00 - 11:30	-
Sem 1 w10	Lecture	Simon TH A	Wed	09:00 - 12:00	-
Sem 1 w11	Lecture	Stopford TH 3	Wed	09:00 - 17:00	-

Themes to which this unit belongs

• Learning from Data

Overview

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 essential algorithms are derived in a step-by-step way as well as how important algorithms can be applied through the use of examples and real world problems. We will cover approaches from machine learning, statistics and neural computation in this advanced machine learning and data mining course unit.

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.

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 methods

Lectures

three hours per week (5 weeks)

Laboratories

three hours per week (5 weeks)

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.

Study hours

Employability skills

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

Learning outcomes

On successful completion of this unit, a student will be able to:

• 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

Reading list

Additional notes

Course unit materials

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