Recent developments in neural network approaches (“deep learning”) have greatly advanced the performance of artificial intelligence systems. This course focuses on the details of these deep learning architectures, including image/audio classification and detection. 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 deep learning applications. The deep learning module is a foundational module that will help students understand the capabilities, challenges, and consequences of deep learning and prepare students to participate in the development of leading-edge AI technology.
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 Understand and participate in the legal, social, ethical and professional framework in systems, software or information engineering. E Demonstrate the ability to work effectively as a member of a development team, recognizing the different roles within the team and the various ways teams can be organized.
This module will be delivered through a combination of formal lectures and lab sessions. The lab experiments will be performed using 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.