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