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MA30326: Machine learning 2

[Page last updated: 22 May 2025]

Academic Year: 2025/26
Owning Department/School: Department of Mathematical Sciences
Credits: 6 [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Honours (FHEQ level 6)
Period:
Semester 1
Assessment Summary: CWRI 100%
Assessment Detail:
  • Coursework (CWRI 100%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: Before taking this module you must take MA22018 OR take MA20278
In taking this module you cannot take MA30279
Learning Outcomes: After taking this unit, you will be able to:
  • Identify and formulate detailed machine-learning problems from practical situations.
  • Judge the suitability of different machine-learning approaches (e.g., architecture, hyperparameter choices, algorithmic efficiency).
  • Train machine-learning models (e.g., for neural networks and tree ensembles).
  • Write code to implement algorithms using Python.



Synopsis: Building on Machine Learning 1, you will further develop knowledge and understanding of Machine Learning by introducing deep neural networks and tree ensembles.

Content: Machine Learning algorithms and mathematics including some of the following: Underlying mathematics: multi-dimensional calculus, training, optimisation, Bayesian modelling, large-scale computation, overfitting and regularisation. Neural networks: dense feed-forward neural networks, convolutional neural networks, autoencoders. Tree ensembles: random forests, gradient boosting. Applications such as image classification. Machine-learning in Python.

Course availability:

MA30326 is Optional on the following courses:

Department of Mathematical Sciences
  • USMA-AFM14 : MMath(Hons) Mathematics (Year 4)
  • USMA-AAM15 : MMath(Hons) Mathematics with Study year abroad (Year 4)
  • USMA-AKM15 : MMath(Hons) Mathematics with Year long work placement (Year 5)

Notes:

  • This unit catalogue is applicable for the 2025/26 academic year only. Students continuing their studies into 2026/27 and beyond should not assume that this unit will be available in future years in the format displayed here for 2025/26.
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