CE802-7-AU-CO:
Machine Learning
2024/25
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 03 October 2024
Friday 13 December 2024
15
25 April 2024
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G41112 Artificial Intelligence,
MSC G411CH Artificial Intelligence,
MSC G51512 Big Data and Text Analytics,
MSC N35012 Artificial Intelligence in Finance,
MSC G40812 Intelligent Systems and Robotics,
MSC G30412 Data Science,
MSC G304PP Data Science with Professional Placement,
MSC G30612 Data Science and its Applications,
MSC G30624 Data Science and its Applications,
MSC G456N2 Computing,
MSC G456N3 Computing,
MSC GH64N4 Computer Systems Engineering,
MPHDG30448 Data Science,
PHD G30448 Data Science,
MSCIN399 Actuarial Science and Data Science,
MSCIG199 Mathematics and Data Science
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:
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Progress Test 1 (In person, MCQ Moodle Test, Closed Book) |
|
30% |
| Coursework |
Progress Test 2 (In person, MCQ Moodle Test, Closed Book) |
|
30% |
| Coursework |
Lab-based coding exercises |
|
40% |
| Exam |
Main exam: In-Person, Open Book (Restricted), 120 minutes during Early Exams
|
| Exam |
Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period)
|
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.
Professor Luca Citi
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
Of 23 hours, 23 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
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