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COMP61021 Modelling and Visualisation of High-Dimensional Data syllabus 2017-2018

COMP61021 materials

COMP61021 Modelling and Visualisation of High-Dimensional Data

Level 6
Credits: 15
Enrolled students: 70

Course leader: Ke Chen


Additional staff: view all staff

Requisites

  • Pre-Requisite (Compulsory): COMP61011

Assessment methods

  • 50% Written exam
  • 50% Coursework
Timetable
SemesterEventLocationDayTimeGroup
Sem 1 P2 Lecture 2.19 Wed 09:00 - 12:00 -
Sem 1 w7-10 Lab 2.25 (A+B) Wed 14:00 - 17:00 -
Sem 1 w11 Lecture 2.19 Wed 13:00 - 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 during on-site marking in the lab.

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

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

Reading list

TitleAuthorISBNPublisherYearCore
Pattern recognition and machine learningBishop, Christopher M.9780387310732Springer2006
Introduction to machine learning (3rd edition)Alpaydin, Ethem9780262028189MIT Press2014
Neural networks and learning machines (3rd edition)Haykin, Simon9780131293762Pearson 2008

Additional notes

Course unit materials

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