Data and Machine Learning

Objectives

  • Provide an overview of Machine Learning, with emphasis on the usefulness and application of different approaches, in particular supervised, unsupervised and reinforced;
  • Understand the challenges inherent in machine learning from data;
  • Process data for training of machine learning systems;
  • Apply the most common learning algorithms, recognizing their domain of application;
  • Implement natural computing models in solving real problems.

Program

  • Data
    • Data, Information and Knowledge
    • Structured, Unstructured, Hybrid Data
  • Data Knowledge Extraction
    • Knowledge Extraction Process Characterization
    • Experimentation with Knowledge Extraction Tools
    • Case Studies and Practical Application
  • Learning Systems
    • Machine Learning
    • Supervised Learning
    • Unsupervised learning
    • Reinforcement Learning
    • Neural Networks
    • Ensemble methods
  • Natural Computing
    • Evolutionary Computing
    • Swarm Intelligence

Bibliography

  • Machine Learning, T. Michell, McGraw Hill, ISBN 978-1259096952, 2017.
  • Introduction to Machine Learning. Alpaydin, E. ISBN: 978-0-262-02818-9. Published by The MIT Press, 2014.
  • Computational Intelligence: An Introduction, Engelbrecht A., Wiley & Sons. 2nd Edition, ISBN 978-0470035610, 2007.
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Hastie, T., R. Tibshirani, J. Friedman; 12nd Edition; Springer; ISBN 978-0387848570, 2016.
  • Machine Learning: A Probabilistic Perspective; K.P. Murphy; 4th Edition; The MIT Press, ISBN 978-0262018029, 2012.

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