CCO - 724 - Machine Learning

Total of Credits: 8
Hours for Theoretical Classes: 60
Hours for Exercises or Seminars: 60

Objective

  • Learning how to implement the most used and state-of-the-art machine learning methods
  • Detecting which method is most suitable to be employed in solving a given type of problem
  • Motivating the practice with case studies.

Catalog Description

  • Basic concepts, history, and notations. Paradigms of learning.
  • Characterization and preparation of data.
  • Supervised learning (distance-based models, probabilistic models, optimization-based models, regression)
  • Unsupervised learning (cluster analysis and algorithms, Dimensionality reduction)
  • Evaluation and Experimental Methodology.
  • Applications
  • State-of-the-art topics in the literature.

Bibliography

  1. Faceli, A.C. Lorena, J. Gama e A.C P.L.F. Carvalho. Inteligência Artificial - Uma Abordagem de Aprendizado de Máquina. LTC. 2011.
  2. Mitchell. Learning Machine. Ed. Mc-Graw Hill. 1997.
  3. Kononenko e M. Kukar. Machine Learning and Data Mining: Introduction to Principles and Algorithms. Horwood Publishing Limited. 2007.
  4. Theodoridis e K. Koutroumbas. Pattern Recognition. Academic Press. 2008.
  5. Artigos que retratem temas relevantes e recentes na área.