This module aims to provide students with a thorough understanding of the principles of deep learning techniques. Students will develop the skills to design, implement, and evaluate deep learning models for a variety of tasks. Through hands-on project work, students will gain experience with the latest deep learning frameworks and tools, as well as learn best practices for data pre-processing, hyperparameter tuning, and debugging. By the end of the module, students will be able to critically evaluate and apply deep learning methods to real-world problems and communicate their findings effectively.
A Demonstrate proficiency in applying concepts and principles of deep learning to design neural network models for complex practical applications. B Analyse different deep learning architectures and techniques and evaluate model performance. C Implement advanced deep learning techniques to improve model performance. D Build, train, and deploy deep neural networks in production environments using industry-standard deep learning practices.
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This philosophy is carried through in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. This module will be delivered through a combination of lectures, group discussions, case studies, and hands-on practical exercises etc. Lectures and group discussions are conducted using the Problem Based Learning paradigm focusing on student-centered learning, where they develop critical thinking and problem-solving skills to address open-ended problems that lacks a straightforward solution This module is taught with an emphasis on student learning through practice and by projects, facilitated by a module leader, and where appropriate, industrial mentors. Students can identify particular areas of learning needs or interests according to the available project(s). They will conduct independent research to gather information and resources to better define the problem. Progress towards the learning outcomes will be facilitated and monitored, where students are guided to progressively address the given problem through tasks. Independent learning will form an important aspect of the educational activities in this module. Case studies will be used to provide students with real-world examples of how the concepts and techniques covered in this module can be applied. Lab/Practical sessions will allow students to apply the techniques and tools acquired to solve real-world industry focused problems. Assessed primarily by a project, students shall gain practical experience in undertaking independent study and research on industry focused real-world problems. This module will leverage generative AI to enhance course content and teaching methods in line with the learning outcomes. By integrating advanced AI technologies, we aim to improve the efficiency of teaching and interaction, while fostering greater student autonomy and flexibility in learning.