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MA50290: Applied machine learning

[Page last updated: 15 August 2024]

Academic Year: 2024/25
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)
Period:
Semester 1
Assessment Summary: CWRI 40%, EXCB 60%
Assessment Detail:
  • Coursework (CWRI 40%)
  • Exam (EXCB 60%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: You must have familiarity with linear algebra (vectors and matrices) and multivariable calculus (especially partial derivatives and the chain rule) to take this module.
Learning Outcomes: By the end of the unit you will be able to:
  • Demonstrate a broad knowledge of common machine learning techniques
  • Understand the mathematics underlying common machine learning techniques
  • Identify and formulate machine learning approaches to solving practical problems



Synopsis: Gain a general foundation in machine learning, covering a range of methodologies for supervised and unsupervised learning. By the end of the unit, you should be able to critically analyse and implement machine learning algorithms, apply them to real-world data, evaluate their performance, and write technical reports to summarise your findings.

Aims: This module will develop students' knowledge and understanding of the theory and practice of a range of machine learning techniques and algorithms used in supervised and unsupervised learning.

Skills: Formulation of machine learning problems TFA, mathematical analysis of machine learning algorithms TFA, computational implementation of machine learning algorithms TFA

Content: Supervised, unsupervised learning and reinforcement learning. Generative and discriminative models. Training. validation. Overfitting, regularisation. Classification, regression. Clustering, dimensionality reduction. The ideas will be explored using some of the following: k-means, nearest neighbours, naive Bayes, logistic regression, decision trees, linear dimensionality reduction, cross validation, stochastic gradient descent

Course availability:

MA50290 is Compulsory on the following courses:

Department of Mathematical Sciences

MA50290 is Optional on the following courses:

Department of Mathematical Sciences
  • RSMA-AFM18 : Integrated PhD Statistical Applied Mathematics

Notes:

  • This unit catalogue is applicable for the 2024/25 academic year only. Students continuing their studies into 2025/26 and beyond should not assume that this unit will be available in future years in the format displayed here for 2024/25.
  • Courses and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.