Module Catalogues

Deep Learning in Computer Vision

Module Title Deep Learning in Computer Vision
Module Level Level 4
Module Credits 5.00
Academic Year 2023/24
Semester SEM1

Aims and Fit of Module

Visual recognition tasks such as image classification, localization and detection are core to many of computer vision applications. Recent developments in neural network approaches (“deep learning”) have greatly advanced the performance of these state-of-the-art visual recognition systems. This course focuses on the details of these deep learning architectures, particularly image classification. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision and multimedia applications.

Learning outcomes

A. Demonstrate expert knowledge to offer critical insight into the current state-of-the-art deep learning technologies. B. Demonstrate practical skills, including the creation of neural networks, and the ability to analyse deep learning algorithm performance. C. Critically analyse real-world computer vision problems and design suitable solutions based on available technologies. D. Demonstrate ability to undertake individual research on computer vision problems using deep learning. E. Understand and participate in the legal, social, ethical and professional framework in systems, software or information engineering. F. Work as a member of a development team recognising the different roles within a team and different ways of organising teams.

Method of teaching and learning

This module will be delivered through a combination of formal lectures and lab sessions. The lab experiments will be performed using C/C++, MATLAB or PYTHON or other software tools for deep learning. For each lab a report shall be prepared, two of these will count towards the summative assessment for the module. A final examination consisting of a number of problem/design based questions as well as questions aimed at examining the students grasp of the subject theory will form the remainder of the summative assessment.