This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.

Postgraduate Handbook 2019-2020 (archived)

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

Department: Mathematical Sciences

MATH51930: Core II A: Advanced Statistics and Machine Learning

Type	Level	Credits	Availability	Module Cap	Location
Tied	5	30	Available in 2019/2020	None.	Durham

Tied To

Pre-requisites

Corequisites

Excluded Combinations

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

Subject-specific Knowledge

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.

Subject-specific Skills

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.

Key Skills

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

Activity	Number	Frequency	Duration	Total Hours
Lectures for Data Acquisition	8	2 per week, weeks 11 - 14, term 2	1 hour	8
Practical classes for Data Acquisition	8	2 per week, weeks 11 - 14, term 2	1 hour	8
Lectures for Advanced Statistics and Machine Learning 1	12	3 per week, weeks 11 - 14, term 2	1 hour	12
Practical classes for Advanced Statistics and Machine Learning 1	4	1 per week, weeks 11 - 14, term 2	1 hour	4
Lectures for Advanced Statistics and Machine Learning 2	12	3 per week, weeks 16 - 19, term 2	1 hour	12
Practical classes for Advanced Statistics and Machine Learning 2	4	1 per week, weeks 16 - 19, term	1 hour	4
Lectures for Advanced Statistics and Machine Learning 3	12	3 per week, weeks 16 - 19, term	1 hour	12
Practical classes for Advanced Statistics and Machine Learning 3	4	1 per week, weeks 16 - 19, term	1 hour	4
Preparation, reading, and self-study				236
Total				300

Summative Assessment

Component	Component Weighting	Element	Length/Duration	Element Weighting
Coursework	100%	Coursework for Data Acquisition	5 weeks	25%
		Coursework for ASML 1	5 weeks	25%
		Coursework for ASML 2	5 weeks	25%
		Coursework for ASML 3	5 weeks	25%

Formative Assessment

Formative feedback is given as part of the coursework feedback.

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.