CE802-7-SP-CO:
Machine Learning
2023/24
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 15 January 2024
Friday 22 March 2024
15
24 July 2023
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G411JS Artificial Intelligence,
MSC G30424 Data Science,
MSC G412JS Artificial Intelligence and its Applications,
MSC G306JS Data Science and its Applications
The module assumes a reasonable programming background and is not suitable for students without prior programming experience.
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
The module assumes a reasonable programming background and is not suitable for students without prior programming experience.
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
Lectures, Labs and Classes.
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 1 |
|
33.33% |
| Coursework |
Progress Test 2 |
|
33.33% |
| Coursework |
Lab-based coding exercises |
|
33.34% |
| Exam |
Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period)
|
| Exam |
Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during January
|
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
Dr Vito De Feo, email: vito.defeo@essex.ac.uk.
Dr Vito De Feo
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
No
No
No
Dr Colin Johnson
University of Nottingham
Dr MARJORY CRISTIANY Da COSTA ABREU
Sheffield Hallam University
Senior Lecturer
Available via Moodle
Of 58 hours, 38 (65.5%) hours available to students:
18 hours not recorded due to service coverage or fault;
2 hours not recorded due to opt-out by lecturer(s), module, or event type.
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