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;
Select, process and process data for training of machine learning systems;
Know and apply the most common learning algorithms, recognizing their domain of application;
Select and 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 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.