Fundamentals of Machine Learning - COM00028H

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  • Department: Computer Science
  • Module co-ordinator: Dr. Suresh Manandhar
  • Credit value: 10 credits
  • Credit level: H
  • Academic year of delivery: 2018-19
    • See module specification for other years: 2019-20

Module summary

The modules covers the basics of machine learning following a Bayesian perspective. It is strongly theory oriented. It requires a good background in calculus particularly differentiation, integration and gradient based methods for finding extremal points of functions.

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Module will run

Occurrence Teaching cycle
A Autumn Term 2018-19
B Autumn Term 2018-19

Module aims

Teach the main theories and techniques of statistical machine learning.

Module learning outcomes

  • Understand basic probability theory
  • Know how to apply Bayesian inference to a given problem
  • Know a selection of techniques which can be applied to learn the models from data

Module content

  • Probability distributions
  • Random variables
  • Joint distributions
  • Bayesian inference
  • Linear regression
  • Logistic regression
  • Regularization
  • Evaluation and cross validation

Assessment

Task Length % of module mark Group
University - closed examination
Fundamentals of Machine Learning (FUML) Exam
1.5 hours 100 Default
University - closed examination
Fundamentals of Machine Learning (FUML) Exam
1.5 hours 100 B

Special assessment rules

None

Reassessment

Task Length % of module mark Group
University - closed examination
Fundamentals of Machine Learning (FUML) Exam
1.5 hours 100 Default
University - closed examination
Fundamentals of Machine Learning (FUML) Exam
1.5 hours 100 B

Module feedback

Students will receive feedback during the Spring term within the allocated time frame for marking.

Indicative reading

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



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.