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Concepts of Machine Learning

Short name: CML
SITS code: BUCI034H6
Credits: 15 credits
Level: 6
Module leader: George Magoulas
Lecturer(s): George Magoulas

Module outline

Using a combination of lectures and lab work, the module covers popular machine learning paradigms giving students knowledge of advanced features of various methods at the theoretical and practical levels.


The module covers computational algorithms for learning from data, data-driven decision making and complex problem solving. It provides an introduction to machine learning methods, such as neural networks, fuzzy logic, fuzzy clustering, natural computing, and covers basic concepts of feature selection and generalisation.

Learning Outcomes

  • Discuss essential facts, concepts, principles and theories of machine learning.
  • Discus fundamental issues relating to the design and implementation of systems that employ machine learning
  • Apply theoretical understanding of machine learning paradigms to solve data modelling, classification and decision making problems.
  • Recognise social, ethical & professional issues and risk involved in the application and use of machine learning methods.
  • Recognise and analyse specifications appropriate to specific problems and plan strategies for their solution.
  • Describe the process involved in the effective deployment of machine learning.
  • Evaluate machine learning methods in terms of general quality attributes and possible trade-offs presented within the given problem.


  • Learning from data
  • Feature selection and generalisation
  • Supervised learning, unsupervised learning and clustering
  • Fuzzy logic and fuzzy clustering
  • Deep networks:architectures and learning algorithms
  • Natural computing: genetic algorithms, evolutionary algorithms, swarm intelligence
  • Advanced learning and evolution schemes


No specific module is pre- or co- requisite but knowledge of mathematical concepts (algebraic concepts, vector, matrix, function and graph, gradient, trigonometry concepts, statistical concepts and the notion of probability) and data structures and algorithms is essential.


All dates and timetables are listed in the programme handbooks of individual programmes.


Two-hour written examination (100%)

Recommended reading

  • S. Marsland, Machine Learning: an Algorithmic Perspective, CRC Press.
  • M. Negnevitsky, Artificial Intelligence: a Guide to Intelligent Systems, Addison Wesley.
  • E. Alpaydin, Introduction to Machine Learning, MIT Press.
  • R. Rojas, Neural Networks-A Systematic Introduction. Available online at:
  • S. Theodoridis, K. Koutroumbas, Pattern Recognition, Academic Press.
  • Students will be pointed to papers available online and other Web resources on the subject.