MATH52115: Advanced Statistics and Machine Learning: Regression and Classification

• PHYS51915 Core Ia: Introduction to Machine Learning and Statistics; PHYS52015 Core Ib: Introduction to Scientific and High-Performance Computing; MATH52015 Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning

• None

• None

• Provide advanced knowledge and critical understanding of the paradigms andfundamental ideas of Bayesian statistics and machine learning.
• Provide advanced knowledge and critical understanding of the methodology and applications of Bayesian statistics and machine learning.

• Advanced statistical modelling (e.g. linear models, graphical models, Gaussian processes).
• Advanced supervised machine learning (e.g. random forests, boosting, deep neural networks, deep statistical models).

Subject-specific Knowledge:

• Advanced understanding of statistical modelling frameworks and methods.
• Advanced understanding of supervised machine learning frameworks and methods.

Subject-specific Skills:

• Ability to use Bayesian theory and inference to frame, analyse, and formalize practical problems, and to reflect critically upon this use.
• Ability to select and apply appropriate computationally-intensive methods to practical problems, and to reflect critically upon their application.
• Ability to select or to develop, and to apply, appropriate models to practical problems, and to reflect critically upon their application.
• Ability to select, adapt, and apply appropriate machine learning methods to practical problems, and to reflect critically upon their application.

Key Skills:

• Lectures demonstrate what is required to be learned and the application of the theory to concrete examples.
• Practical classes concretize understanding via the application of calculational and computational methods to more complex problems, as well as providing feedback and encouraging active engagement.
• Coursework will assess students' ability to implement calculational and computational methods on both synthetic and real problems.

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