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MA50263: Mathematics of machine learning

Follow this link for further information on academic years Academic Year: 2019/0
Further information on owning departmentsOwning Department/School: Department of Mathematical Sciences
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 1
Further information on unit assessment Assessment Summary: CW 100%
Further information on unit assessment Assessment Detail:
  • Computational Project (CW 50%)
  • Research Project (CW 50%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites: Must have Programming ability in Python or other high-level language. Graduate level mathematics skills.
In taking this module you cannot ( take CM50264 OR take CM50265 )
Further information on descriptions Description: Aims:
To teach Machine Learning, including theoretical background and tools for implementation, to statistical applied mathematicians.

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.

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.
Further information on programme availabilityProgramme availability:

MA50263 is Optional on the following programmes:

Department of Mathematical Sciences

Notes: