Supervised Learning
- Details
- Category: Discipline
- 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.
- Data Science
- 6321
- 23087
- Supervised Learning
- ISMAT6321-23087
- 2
- 8
- 0
- 12
- Não
- Português
- 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.
- Mandatory
- 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.
- 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
A avaliação de conhecimentos é feita por avaliação contínua ou por prova escrita de exame final. A avaliação contínua inclui a realização de um teste escrito (50%) e um trabalho de grupo (50%).
- Semestral
- 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.
