Module MATH52015: Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning

Department: Mathematical Sciences

MATH52015: Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning

Prerequisites

Corequisites

• PHYS51915 Core Ia: Introduction to Machine Learning and Statistics AND PHYS52015 Core Ib: Introduction to Scientific and High-Performance Computing

Excluded Combination of Modules

Aims

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

Content

• Bayesian theory, inference, and computation (e.g. foundations, probability and decision theory, sampling methods, variational methods).
• Unsupervised learning (e.g. density estimation, kernels, clustering, EM, etc.)

Learning Outcomes

• Advanced understanding of Bayesian theory, inference, and computationally-intentensive methods and algorithms.
• Advanced understanding of unsupervised machine learning frameworks and methods.

• 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.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• 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.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

■ Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.