Checking date: 08/04/2019

Course: 2019/2020

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

Coordinating teacher: NOGALES MARTIN, FCO. JAVIER

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 3.0 ECTS


Competences and skills that will be acquired and learning results.
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
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 (50% of the sessions): the contents of the course will be introduced, explained and illustrated with examples. Teaching materials will be provided on Aula Global. Computer Labs (50% of the sessions): Examples and cases studies with the R language.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
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 and the academic weekly planning may change due academic events or other reasons.