Course overview
This course aims to equip learners with the advanced knowledge and skills needed to design, analyse, and critically evaluate advanced machine learning and artificial intelligence systems, including federated learning, reinforcement learning, adversarial machine learning, and secure and responsible AI. By engaging with both theoretical foundations and cutting-edge research, learners will develop the ability to identify risks such as bias, fairness, and security threats in large-scale AI models, while also acquiring practical expertise in developing robust, interpretable, and ethically responsible AI solutions. This course aligns with the program’s intent to prepare graduates as technically skilled, research-informed, and ethically aware AI professionals who can contribute to advancing trustworthy and responsible AI systems across academia, industry, and society.
- Advanced Machine Learning Paradigms
- Secure and Trustworthy Machine Learning
- Transparent and Responsible Artificial Intelligence
Course learning outcomes
- Analyse advanced machine learning and artificial intelligence systems, including models, emerging topics and practical applications.
- Identify ethical, societal and technological risks and security threats in large-scale AI models and propose relevant mitigations.
- Explore literature and conduct research relating to artificial intelligence and machine learning.
- Develop prototype solutions to real-world problems using machine learning and artificial intelligence algorithms and approaches.