Machine Learning and Data Mining
Computer Science and Electronic Engineering (School of)
Postgraduate: Level 7
Thursday 03 October 2019
Saturday 14 December 2019
08 May 2019
Requisites for this module
MSC G10124 Mathematics,
MSC G41112 Artificial Intelligence,
MSC G51512 Big Data and Text Analytics,
MSC G40812 Intelligent Systems and Robotics,
MSC G30412 Data Science,
MSC G30424 Data Science,
MSC G304PP Data Science with Professional Placement
This module provides an understanding of machine learning, the methods involved in evaluating them, and their application to real-world problems. It will include classification and regression learning along with other techniques, and apply the techniques to particular classes of problems.
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:
1. demonstrate an understanding of the major approaches to classification and regression learning
2. demonstrate an understanding of other machine learning techniques that have important practical applications
3. identify machine learning techniques appropriate for particular classes of problem and apply them to practical problems
4. 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
- Q learning
- Bagging, boosting, forests and stacking
Lectures, Labs and Classes.
- Geron, Aurelien. (2019) Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, Sebastopol: O'Reilly Media, Inc, USA.
- Alpaydin, Ethem. (©2014) Introduction to machine learning, Cambridge, Massachusetts: The MIT Press.
The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.
Assessment items, weightings and deadlines
|Coursework / exam
||Progress Test 1 - Week 8
||Progress Test 2 - Week 11
||Assignment 1 - Report on Practical Exercise
||180 minutes during Early Exams (Main)
Module supervisor and teaching staff
Prof Luca Citi, email: firstname.lastname@example.org.
Dr Luca Citi
School Office, e-mail csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770.
Dr Robert Mark Stevenson
University of Sheffield
Available via Moodle
Of 97 hours, 30 (30.9%) hours available to students:
67 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
Disclaimer: The University makes every effort to ensure that this information on its Module Directory is accurate and up-to-date. Exceptionally it can
be necessary to make changes, for example to programmes, modules, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements,
industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to modules may for example consist
of variations to the content and method of delivery or assessment of modules and other services, to discontinue modules and other services and to merge or combine modules.
The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications and module directory.
The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.