Neural networks and deep learning

This unit introduces the fundamentals of deep learning and its applications across various domains, including image classification, signal processing, and natural language understanding. Neural networks are first described, followed by how training can be achieved with backpropagation. Various forms of deep neural networks are developed, including Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Modern advancements such as transformers and Large Language Models (LLMs) are described, as well as their deployment and fine-tuning. The mathematics of optimization and generalization is used to interpret and understand the behaviour and training of these networks. Programming frameworks for training, fine-tuning, and deploying neural networks are discussed. Deep learning technologies and design examples are discussed in areas such as visual perception, driverless cars, intelligent assistants, and generative AI.

Online learning - Lectures will be delivered online

Problem-based learning - Pracs will be delivered online

Active learning - The theory and concepts of deep learning and neural networks covered in the lectures are practised in the 2-hour workshops. This includes the design of new algorithms and applications based on the theory and problem-solving classes. We will host industry experts to provide guest lectures at the end of the semester.

The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.