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ISMAT 23087

Supervised Learning

Data Science
  • ApresentaçãoPresentation
    The curricular unit Supervised Learning belongs to the scientific area of Data Science and addresses the main supervised machine learning methods for classification and regression problems. It develops skills in building, evaluating and interpreting predictive models, which are essential for solving problems across different application domains.  
  • ProgramaProgramme
    S1: Introduction to supervised data learning and its applications S2: Regression methods S3: Classification methods S4: Evaluation and performance comparison of supervised learning models S5: Using software, such as R, Python and SAS  
  • ObjectivosObjectives
    At the end of this course unit, students should be able to: LO1: Distinguish and compare supervised learning techniques, in classification and regression tasks; LO2: Adjust supervised learning models to data and use them for predictive purposes; LO3: Evaluate and compare the performance of supervised learning models; LO4: Use computational resources, such as R, Python and SAS.  
  • BibliografiaBibliography
    Hair, J.F., Tatham, R.L., Anderson, R.E. & Black, W. (2009). Análise multivariada de dados. (6ª ed.). Porto Alegre: Bookman.  James, G., Witten, D., Hastie, T., Tibshirani, R. (2013), An Introduction to Statistical Learning: with applications in R, New York: Springer. Maindonald, J. & Braun, W. J. (2010). Data analysis and graphics using R: an example-based approach. (3rd ed.). United Kingdom: Cambridge University Press.  
  • MetodologiaMethodology
    The teaching methodology includes the expository method (TM1) to present the contents, the demonstrative method (TM2) to illustrate its application to practical cases and the active method (TM3) to solve exercises, with the use of a computer, and find solutions to proposed problems (problem based learning). real datasets and machine learning tools are used to develop, compare and interpret predictive models across different application contexts.    
  • LínguaLanguage
    Português
  • TipoType
    Semestral
  • ECTS
    8
  • NaturezaNature
    Mandatory
  • EstágioInternship
    Não