CSC-20071 - Module Specification School of Computer Science and Mathematics Faculty of Natural Sciences
For academic year: 2024/25 Last Updated: 21 November 2024
CSC-20071 - Applied Deep Learning
Coordinator: Baidaa Al-Bander
School Office: 01782 733075
Programme/Approved Electives for 2024/25
Available as a Free Standing Elective
Co-requisites
None
Prerequisites
CSC-10058 Introduction to Data Science ICSC-10060 Introduction to Data Science IIMAT-10055 Mathematical Techniques for Data Science
Barred Combinations
None
Description for 2024/25
This module will provide students with an introduction to deep learning, with a focus on understanding its capabilities and writing programs that use appropriate software libraries to apply deep learning to tasks such as text analysis, computer vision, image processing and pattern recognition.
The aim of the module is to provide students with an introduction to deep learning, with a focus on understanding its capabilities and writing programs that use a software library to apply deep learning to tasks such as pattern recognition and classification when applied to text processing, computer vision and image processing.
Intended Learning Outcomes
Identify and describe the capabilities and limitations of deep neural networks, including convolutional neural networks and long short term memory networks: Develop software that uses appropriate libraries to create, train and evaluate deep neural networks: Apply deep learning to tasks such as computer vision and textual sentiment analysis using techniques such as transfer learning:
Study hours
14 hours lectures20 hours practical classes26 hours coursework preparation90 hours private study
School Rules
CSC-20043 Computational and Artificial Intelligence 1
Description of Module Assessment