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.
- 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
||120 minutes during Early Exams (Main)
Module supervisor and teaching staff
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
Available via Moodle
Of 50 hours, 32 (64%) hours available to students:
18 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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