Module Details

CE802-7-AU-CO: Machine Learning And Data Mining

Year: 2016/17
Department: Computer Science and Electronic Engineering
Essex credit: 15
ECTS credit: 7.5
Available to Study Abroad / Exchange Students: No
Full Year Module Available to Study Abroad / Exchange Students for a Single Term: No
Outside Option: No

Staff
Supervisor: Dr Luca Citi
Teaching Staff: Dr Luca Citi
Contact details: School Office, e-mail csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770.

Module is taught during the following terms
Autumn Spring Summer

Module Description

Module Description

The aim of this module is to provide an understanding of the major approaches to machine learning, the methods involved in evaluating them, and their application to the solution of real problems.

LEARNING OUTCOMES

On completion of the course, students should be able to:

- demonstrate an understanding of the major approaches to classification and regression learning

- demonstrate an understanding of other machine learning techniques that have important practical applications

- identify machine learning techniques appropriate for particular classes of problem and apply them to practical problems

- undertake a comparative evaluation of several machine learning procedures

SYLLABUS

Introduction

- What is meant by machine learning

- Taxonomy of machine learning algorithms

- The inductive bias

- Data mining

Learning to classify:

- Decision tree induction

- Naïve Bayes methods

- Bayesian networks

- K-nearest neighbour method

- Support vector machines

Learning to predict numeric values:

- Linear Regression

- Regression trees

Evaluating learning procedures

Overfitting and the 'bias-variance trade-off'

Applications of machine learning

Clustering:

- k-means algorithm

- agglomerative hierarchical methods

Association rules mining:

- A priori algorithm

Reinforcement learning:

- Q learning

Multiple learners:

- Bagging, boosting, forests and stacking

Learning and Teaching Methods

Lectures, Labs and classes

Assessment

50 per cent Coursework Mark, 50 per cent Exam Mark

Coursework

There will be two Progress Tests with a weighting (as percentage of module mark) of 15% each which will take place in weeks 7 and 11. The assignment, Report on Practical Exercise, with a weighting (as percentage of module mark) of 20%. This will be issued in week 10 and submitted to FASER in week 16.

Other information

STUDENTS SHOULD NOTE THAT THIS MODULE INFORMATION IS SUBJECT TO REVIEW AND CHANGE

Competence in programming in a procedural language. GCSE level mathematics.

Bibliography

  • TAN, P-N., STEINBACH, M. and KUMAR, V., Introduction to Data Mining, Addison Wesley, 2006
  • HAN, J. & KAMBER, M., Data Mining: Concepts and Techniques (2nd ed), Morgan Kaufmann 2006
  • WITTEN, I. H. and FRANK, E., Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (2nd ed), Morgan Kaufmann 2005
  • MITCHELL, T. M., Machine Learning, McGraw-Hill, 1997
  • ALPAYDIN, E., Introduction to Machine Learning (2nd Ed), MIT Press 2010