Computer Science and Electronic Engineering (School of)
Undergraduate: Level 6
Thursday 05 October 2023
Friday 15 December 2023
30 March 2022
Requisites for this module
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.
After completing this module, students will be expected to be able to:
1. Demonstrate a conceptual understanding of the main methods for classification and regression learning tasks
2. Undertake a critical evaluation of several machine learning procedures
3. Demonstrate detailed knowledge of machine learning techniques
4. Identify and implement machine learning algorithms appropriate for practical problems
Classification and Regression:
Support vector machines
Neural network architectures
Gradient descent learning algorithms
Learning method evaluation:
Recall and Precision
Deep neural networks:
Feature extraction with convolutional operations
Recurrent neural network
Variational auto-encoders (VAEs) and generative adversarial networks (GANs)
Deep learning applications:
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.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
|Coursework / exam
|Main exam: In-Person, Open Book (Restricted), 180 minutes during Early Exams
|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.
Module supervisor and teaching staff
Prof Sandra Dudley
London South Bank University
Professor of Communication Systems
Available via Moodle
No lecture recording information available for this module.
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