Checking date: 28/05/2021

Course: 2021/2022

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


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
Skills and learning outcomes
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
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.

More information: Aula Global