CE802-7-AU-CO:
Machine Learning and Data Mining
2017/18
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 05 October 2017
Friday 15 December 2017
15
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Requisites for this module
(none)
(none)
(none)
(none)
(none)
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
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
No information available.
No information available.
No additional information available.
Lectures, Labs and Classes.
- Alpaydin, Ethem. (2014) Introduction to machine learning, Cambridge, Massachusetts: The MIT Press. vol. Adaptive computation and machine learning
- Alpaydin, Ethem. (2010) Introduction to Machine Learning, Cambridge, Mass.: MIT Press Ltd.
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 |
Coursework weighting |
| Coursework |
Progress Test week 7 |
|
30% |
| Coursework |
Progress Test week 11 |
|
30% |
| Coursework |
Assignment - Report on Practical Exercise |
|
40% |
| Exam |
Main exam: 120 minutes during Early Exams
|
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
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.
Yes
No
Yes
Dr Robert Mark Stevenson
University of Sheffield
Senior Lecturer
Available via Moodle
Of 42 hours, 30 (71.4%) hours available to students:
12 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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