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: Yes
Full Year Module Available to Study Abroad / Exchange Students for a Single Term: No
Outside Option: No

Supervisor: Dr Luca Citi
Teaching Staff: Dr Luca Citi
Contact details: School Office, e-mail csee-schooloffice (non-Essex users should add 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.


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



- 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


- 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


50 per cent Coursework Mark, 50 per cent Exam Mark


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.