Module MATH52115: Advanced Statistics and Machine Learning: Regression and Classification

Department: Mathematical Sciences

MATH52115: Advanced Statistics and Machine Learning: Regression and Classification

Type	Level	Credits	Availability	Module Cap	Location
Tied	5	15	Available in 2026/2027	None.	Durham

Tied To

G5T109: Scientific Computing and Data Analysis (Earth and Environmental Sciences) (see Postgraduate Handbook 2025-2026 for details)
G5T209: Scientific Computing and Data Analysis (Financial Technology)
G5T309: Scientific Computing and Data Analysis (Astrophysics)
G5T509: Scientific Computing and Data Analysis (Computer Vision and Robotics)
G5T809: Scientific Computing and Data Analysis (Artificial Intelligence for Engineering)
G5T909: Scientific Computing and Data Analysis (Environmental and Geographic Information Systems)
G5TA09: Scientific Computing and Data Analysis (AI Infrastructure Platforms)

Pre-requisites

Corequisites

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

Excluded Combinations

None

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

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).

Learning Outcomes

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

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	Attendance Monitored
Lectures	30	3 per week, weeks 11 - 20, term 2	1 hour	30
Practicals	10	1 per week, weeks 11 - 20, term 2	1 hour	10	Yes
Preparation and Reading				110
Total				150

Summative Assessment

Component	Component Weighting	Element	Length/Duration	Element Weighting	Resit Opportunity
Coursework	100%	In-Year Test	2 hours	50%
Coursework	100%	In-Year Test	2 hours	50%

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.