Course overview
This course aims to equip learners with the advanced mathematical knowledge and skills that underlie modern artificial intelligence. Building up the mathematical knowledge they will have attained in multivariable calculus, algebra and probability, learners will learn methods like advanced linear algebra, tensors, as well as introductory information theory and signal processing, and how they are applied in modern AI tools like deep learning, computer vision and transformer-based models. They will lean how to implement these modern AI models and architectures using programming approaches like PyTorch, how to interpret those models and communicate them to a general audience.
- Foundations
- Deep Learning
- State-Of-The-Art
Course learning outcomes
- Describe the fundamental architectures of deep learning and AI models and connect this with the underlying mathematical principles
- Demonstrate an understanding of how to represent and model data using deep learning approaches
- Demonstrate skills in training and predicting using deep learning using computer programming, including visualisation
- Apply mathematical deep learning techniques to solve a variety of problems at an appropriate level of difficulty
- Demonstrate skills in communicating about artificial intelligence and deep learning, including through designing appropriate numerical experiments and visualisations
Degree list
The following degrees include this course