Module MATH51930: Core II A: Advanced Statistics and Machine Learning

Department: Mathematical Sciences

MATH51930: Core II A: Advanced Statistics and Machine Learning

Prerequisites

Corequisites

Excluded Combination of Modules

Aims

• Provide basic knowledge and critical understanding of approaches to data acquisition.
• 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

• Principles and practice of data acquisition.
• Bayesian theory and inference.
• Statistical modelling (e.g. linear models, graphical models, Gaussian processes and kernel methods).
• Computationally-intensive methods (e.g. sampling methods, variational methods).
• Advanced machine learning (e.g. random forests, boosting, deep neural networks, deep statistical models).

Learning Outcomes

• An understanding of the principles and practice of data acquisition.
• Advanced understanding of Bayesian theory and inference.
• Advanced understanding of statistical modelling frameworks and methods.
• Advanced understanding of computationally-intentensive methods and algorithms.
• Advanced understanding of machine learning frameworks and methods.

• Specific skills generally used in practical data acquisition.
• Ability to use Bayesian theory and inference to frame, analyse, and formalize practical problems, and to reflect critically upon this use.
• Ability to select or to develop, and to apply, appropriate models to practical problems, and to reflect critically upon their application.
• Ability to select and apply appropriate computationally-intensive methods 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:

Formative feedback is given as part of the coursework feedback.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University