Machine Learning and Data Mining

The details
Computer Science and Electronic Engineering (School of)
Colchester Campus
Postgraduate: Level 7
Thursday 03 October 2019
Saturday 14 December 2019
08 May 2019


Requisites for this module



Key module for

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

Module description

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.

Module aims

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.

Module learning outcomes

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

Module information



- 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

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 Description Deadline Weighting
Coursework   Progress Test 1 - Week 8     30% 
Coursework   Progress Test 2 - Week 11     30% 
Coursework   Assignment 1 - Report on Practical Exercise     40% 
Exam  180 minutes during Early Exams (Main) 

Overall assessment

Coursework Exam
50% 50%


Coursework Exam
50% 50%
Module supervisor and teaching staff
Prof Luca Citi, email: lciti@essex.ac.uk.
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.



External examiner

Dr Robert Mark Stevenson
University of Sheffield
Senior Lecturer
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).


Further information

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