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Programme & Unit Catalogues

MA50263: Mathematics of machine learning

[Page last updated: 04 February 2022]

Academic Year: 2021/2
Owning Department/School: Department of Mathematical Sciences
Credits: 6 [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Semester 2
Assessment Summary: CW 100%
Assessment Detail:
  • Computational Project (CW 30%)
  • Research Project (CW 70%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: Must have Programming ability in Python or other high-level language. Graduate level mathematics skills.
In taking this module you cannot take CM50265
Learning Outcomes: After taking this unit, students should be able to:
* Demonstrate knowledge of modern machine learning techniques
* Use computational tools for applying machine learning
* Show awareness of the applications of these methods
* Understand the mathematical models underlying machine learning algorithms and details of their implementation
* Write the relevant mathematical arguments in a precise and lucid fashion.

Aims: To teach Machine Learning, including theoretical background and tools for implementation, to statistical applied mathematicians.

Skills: Problem Solving (T,F&A), Computing (T,F&A), independent study and report writing.

Content: Introduction to machine learning (supervised vs unsupervised learning, generative vs discriminative models, validation, regression vs neural networks, computational tools in Python).
Additional topics will be chosen from:
* Neural networks (feed-forward, convolution, recurrent networks). Universal approximation theorem. Gradient descent
* Graphical models (decision trees, random forests, Markov random fields, Boltzmann machines)
* Bayesian non-parametric (Gaussian and Dirichlet process regression, hyper parameters)
* Reinforcement learning
* Shrinkage methods.

Programme availability:

MA50263 is Optional on the following programmes:

Department of Mathematical Sciences


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