CE326-6-AU-CO:
Machine Learning
2024/25
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Undergraduate: Level 6
Current
Thursday 03 October 2024
Friday 13 December 2024
15
05 September 2024
Requisites for this module
(none)
(none)
(none)
(none)
(none)
BSC I400 Artificial Intelligence,
BSC I401 Artificial Intelligence (Including Foundation Year),
BSC I402 Artificial Intelligence (including Placement Year),
BSC I403 Artificial Intelligence (including Year Abroad)
This module provides an introduction to machine learning techniques, ranging from supervised learning, unsupervised learning, and deep learning.
Also included are the evaluation metrics and procedures of various learning methods. The module focuses on classification and regression learning tasks, and their applications to real-world problems.
The aim of this module is:
- To provide an understanding of the main machine learning methods, the evaluation metrics for learning methods, and the applications to real-world problems.
By the end of this module, students will be expected to be able to:
- Demonstrate a conceptual understanding of the main methods for classification and regression learning tasks.
- Undertake a critical evaluation of several machine learning procedures.
- Demonstrate detailed knowledge of machine learning techniques.
- Identify and implement machine learning algorithms appropriate for practical problems.
Outline syllabus:
- Introduction
- Classification and Regression:
- Bayesian classifiers
- Support vector machines.
- Neural network architectures
- Gradient descent learning algorithms
- Learning method evaluation:
- Cross-validation
- Confusion matrices.
- Recall and Precision
- Unsupervised Learning:
- Association rules
- K-means method.
- Deep neural networks:
- Feature extraction with convolutional operations
- CNN architectures .
- Deep learning applications:
- Image classification
- Image segmentation.
Every lecture will be followed by a lab session where the ideas will be put into practice.
Inclusivity is ensured in the following ways: lecturers and other teachers are informed at the start of the term about students with special needs; student voice groups allow representatives to discuss issues surrounding learning for minorities.
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 (In person, closed book) |
|
25% |
Coursework |
Machine Learning Exercise |
10/12/2024 |
75% |
Exam |
Main exam: In-Person, Open Book (Restricted), 180 minutes during Early Exams
|
Exam |
Reassessment Main exam: In-Person, Open Book (Restricted), 180 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
Dr Rab Nawaz, email: rab.nawaz@essex.ac.uk.
Dr Rab Nawaz
csee-schooloffice@essex.ac.uk
No
No
Yes
Prof Sandra Dudley
London South Bank University
Professor of Communication Systems
Available via Moodle
Of 24 hours, 12 (50%) hours available to students:
11 hours not recorded due to service coverage or fault;
1 hours not recorded due to opt-out by lecturer(s), module, or event type.
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