Machine Learning & Applications - COM00010H

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  • Department: Computer Science
  • Module co-ordinator: Dr. Suresh Manandhar
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2017-18

Module occurrences

Occurrence Teaching cycle
A Autumn Term 2017-18 to Summer Term 2017-18

Module aims

The aims of the module are to:

  • Teach the main theories and techniques of statistical machine learning and pattern recognition.
  • Apply such techniques to various problem areas such as graphical models and recognition.

Module learning outcomes

On completion of this module, students will:

  • Understand basic probability theory
  • Know how to apply Bayesian inference to learn about a problem
  • Be able to construct probability models of particular problems
  • Know a selection of techniques which can be applied to learn the models from data
  • Understand how these methods can be used in particular application areas

Module content

Prerequisite knowledge

A good background in the following topics (covered in the NUMA module) is strongly needed for MLAP:
-- differentiation and partial differentation of functions
-- use of differential equations to solve equations and finding extremum points
-- knowledge of vector and matrix calculus
-- fitting data
-- multivariate optimisation
-- constrained optimisation

Joint CS/Maths students should ensure that they have studied the above topics if they have not taken NUMA.

Assessment

Task Length % of module mark
Essay/coursework
Machine Learning & Applications (MLAP) Report
N/A 40
University - closed examination
Machine Learning & Applications (MLAP)
2 hours 60

Special assessment rules

None

Reassessment

Task Length % of module mark
Essay/coursework
Machine Learning & Applications (MLAP) Report
N/A 40
University - closed examination
Machine Learning & Applications (MLAP)
2 hours 60

Module feedback

In the Autumn and Spring terms, students do practical exercises and problem classes. There will be appropriate associated formative feedback.

Key texts

**** Kevin Murphy, Machine Learning: a Probabilistic Perspective, MIT Press, 2012

*** Hastie, T., Tibshirani, R., and R. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition., Springer, 2009

*** David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.