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;
• 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
• Learning Systems
• Machine Learning: Supervised, Unsupervised and Reinforcement Learning Neural Networks Ensemble methods
• Natural Computing: Evolutionary Computing Swarm Intelligence

Bibliography

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