Machine Learning and Data Mining (MLDM-DAB)
Área
AC Gestão > UC Mestrados
Activa nos planos curriculares
Data Analytics for Business > Data Analytics for Business > 2º Ciclo > Unidades Curriculares Obrigatórias > Machine Learning and Data Mining
Nível
2º Ciclo (M)
Tipo
Estruturante
Regime
Semestral
Carga Horária
Aula Teórica (T): 0.0 h/semana
Aula TeoricoPrática (TP): 3.0 h/semana
Trabalho Autónomo: 129.0 h/semestre
Créditos ECTS: 6.0
Objectivos
To provide a solid knowledge on algorithms for the analysis of large volumes of data using specialized software for data science.
Programa
1.Supervised learning: regression and classification
2.Regularized linear models: ridge, lasso and elastic net.
3.k-nearest neighbors
4.Decision trees
5.Naïve Bayes methods
6.Support vector machines
7.Neural networks and deep learning
8.Ensemble methods: bagging, boosting and random forests
9.Unsupervised learning
Metodologia de avaliação
The teaching methodology is of theoretical and practical nature. In the class, the theoretical concepts of the material are introduced. Concomitantly, illustrative problems are presented and solved using specialized software for statistical analysis.
The final grade is awarded based on a written examination.
Bibliografia
Principal
An Introduction to Statistical Learning with Applications in R
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
2013
1st ed., Springer Texts in Statistics
Machine Learning
Peter Flach
2012
Cambridge University Press,
Machine learning with R
Brett Lantz
2013
Packt Publishing Limited
Secundária
Não existem referências bibliográficas secundárias.