CE802-7-SP-CO:
Machine Learning
2024/25
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 13 January 2025
Friday 21 March 2025
15
25 April 2024
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G411JS Artificial Intelligence,
MSC N35012 Artificial Intelligence in Finance,
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.
By the end of this module, students will be expected to be able to:
- Demonstrate a conceptual understanding of the theoretical foundations behind machine learning methods and design machine learning methods to solve practical problems.
- Demonstrate a systematic understanding of various machine learning techniques, including supervised, unsupervised, and reinforcement learning methods.
- Critically evaluate the suitability and limitations of different machine learning algorithms for specific classes of practical problems.
- Critically assess the performance of machine learning models in various contexts, using appropriate methods to obtain performance estimates and prevent data leakage issues.
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:
Reinforcement learning:
Multiple learners:
- Bagging, boosting, forests and stacking
This module will be delivered via:
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 |
|
30% |
| Coursework |
Progress Test 2 |
|
30% |
| Coursework |
Lab-based coding exercises |
|
40% |
| 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
Yes
Dr Colin Johnson
University of Nottingham
Dr MARJORY CRISTIANY Da COSTA ABREU
Sheffield Hallam University
Senior Lecturer
Available via Moodle
No lecture recording information available for this module.
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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