Currículo
Big Data BIGD
Contextos
Groupo: Actuarial Science > 2º Ciclo > Parte Escolar > Unidades Curriculares Optativas
ECTS
6.0 (para cálculo da média)
Objectivos
By the end of this course, the students should understand some popular algorithms used in the analysis of large volumes of data, and the tools for analyzing data in a Big Data project.
Programa
Introduction to forecasting with machine learning. Linear models and regularization. Logistic regression. k-nearest neighbors. Decision trees. Naive Bayes methods. Support vector machines. Ensemble methods. Neural networks and deep learning. Unsupervised learning methods. Big Data projects.
Método de Avaliação
Written exam.
Carga Horária
Carga Horária de Contacto -
Trabalho Autónomo - 117.0
Carga Total -
Bibliografia
Principal
- An Introduction to Statistical Learning with Applications in R: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. 2021 2nd ed., Springer Texts in Statistics
Secundária
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction: Trevor Hastie, Robert Tibshirani, Jerome Friedman 2009 2nd ed., Springer Texts in Statistics