DML

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; Know the most common learning algorithms, recognizing their domain of application; Implement natural computing models in solving real problems.

Program

1 - Data Data, Information and Knowledge Structured, Unstructured, Hybrid Data 2 - Data Knowledge Extraction Knowledge Extraction Process Characterization Experimentation with Knowledge Extraction Tools Case Studies and Practical Application 3 - Learning Systems 4 - Machine Learning Supervised Learning Unsupervised learning Reinforcement Learning Neural Networks Ensemble methods 5 - Natural Computing Evolutionary Computing Swarm Intelligence

Bibliography

Machine Learning, T. Michell, McGraw Hill, ISBN 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.