ECE3192 · Fundamentals of deep learning

Overview

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), 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 stochastic optimisation and generalisation 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.

Areas of study: E3001 Bachelor of Engineering (Honours) - Specialisation: Biomedical engineering E3001 Bachelor of Engineering (Honours) - Specialisation: Robotics and mechatronics engineering Minor: Internet of Things (IoT) Minor: Artificial intelligence in engineering

Offerings

Assessment

The Handbook lists an examination for this unit.

Continuous assessment: 50% Final assessment (2 hours and 10 minutes): 50% Assessment in this unit includes competency hurdle assessment tasks. The consequence of not achieving a competency hurdle is a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Discuss the mathematical concepts and fundamental algorithms which underpin deep learning.
2. Discern and appreciate various forms of deep neural networks.
3. Appraise sources of information and critically weigh the risk and requirements for use of AI in real world applications.

Workload

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.

Student experience, not official rules. Nothing here changes what the Handbook says.