Checking date: 08/04/2019

Course: 2019/2020

Statistical Learning
Study: Master in Statistics for Data Science (345)

Coordinating teacher: NOGALES MARTIN, FCO. JAVIER

Department assigned to the subject: Department of Statistics

Type: Compulsory
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 Know how to evaluate supervised-learning models Develop skills to classify observations based on probabilistic learning and machine learning tools Handle the R language for statistical-learning tools
Description of contents: programme
Introduction to Statistical Learning Performance Evaluation of Learning Models Bayesian Learning Bayes Rule and Cost-Sensitive Learning k-NN Support Vector Machines Decision Trees and Random Forests Neural Networks
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.