Course overview
This course focuses on the theoretical foundations of machine learning. It aims to equip students with the knowledge and skills of the mathematical underpinnings of machine learning algorithms, including linear algebra, calculus, probability, information theory and optimisation, and to develop students understanding and ability on how to apply the mathematical concepts and theories to the design of machine learning algorithms for solving fundamental machine learning problems such as regression, classification, dimensionality reduction and density estimation.
- Mathematical foundations for machine learning
- Basics of machine learning algorithms
- Applications: example algorithms for solving the central machine learning problems
Course learning outcomes
- Describe the mathematical concepts that form the foundations of different machine learning algorithms.
- Explain the key components of a machine learning algorithm and the major principles of model learning and model selection
- Analyse and critique machine learning algorithms and their applications with the learned mathematics language
- Apply the learned mathematical foundations and principles for model learning and selection to solve machine learning problems.
Degree list
The following degrees include this course