Checking date: 22/07/2021

Course: 2021/2022

Advanced Regression and Prediction
Study: Master in Statistics for Data Science (345)

Coordinating teacher: MOLINA PERALTA, ISABEL

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 3.0 ECTS


Become familiar with different analytical tools, based on data, to make business decisions Capacity to develop skills to analyze and find relationships between many variables/features Relax some of the assumptions in classical linear regression Deal with the curse of dimensionality in high-dimensional problems Acquire knowledge about the main tools in advanced predictive tools and handle the R language with those models
Skills and learning outcomes
Description of contents: programme
Introduction Feature Engineering: non-linearities and interactions Efficient Estimation in Least-Squares (QR and SVD) Robustness Variable Selection Regularization tools (shrinkage) Dimension-reduction techniques k-NN Decision Trees and Random Forests
Learning activities and methodology
Lectures: the contents of the course will be introduced, explained and illustrated with examples. Teaching materials will be provided on Aula Global. Computer Labs: Examples and cases studies with the R language.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
Basic Bibliography
  • G. James, D. Witten, T. Hastie and R. Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer. 2013
  • Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press. 2012
  • Machine Learning with R. Brett Lantz. Packt Publishing. 2015

The course syllabus may change due academic events or other reasons.