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MATH52115: Advanced Statistics and Machine Learning: Regression and Classification

It is possible that changes to modules or programmes might need to be made during the academic year, in response to the impact of Covid-19 and/or any further changes in public health advice.

Type Tied
Level 5
Credits 15
Availability Available in 2023/24
Module Cap None.
Location Durham
Department Mathematical Sciences

Prerequisites

  • 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

Corequisites

  • None

Excluded Combinations of Modules

  • None

Aims

  • 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.

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

ActivityNumberFrequencyDurationTotalMonitored
Lectures on Regression123 per week, weeks 16 - 19, term 21 hour12 
Practical classes on Regression41 per week, weeks 16 - 19, term 21 hour4 
Lectures on Classification123 per week, weeks 16 - 19, term 21 hour12 
Practical classes on Classification41 per week, weeks 16 - 19, term 21 hour4 
Preparation, reading, and self-study118 

Summative Assessment

Component: CourseworkComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Coursework on Regression5 weeks50 
Coursework on Classification5 weeks50 

Formative Assessment

More information

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