Checking date: 27/06/2025 17:57:56


Course: 2025/2026

Statistical Learning
(17757)
Master in Statistics for Data Science (Plan: 386 - Estudio: 345)
EPI


Coordinating teacher: DELGADO GOMEZ, DAVID

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Mathematics for Data Science Probability Programming in R Statistical Inference Advanced Programming Multivariate Analysis Regression Models
Objectives
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 and Python languages for statistical-learning tools
Learning Outcomes
Description of contents: programme
1. Introduction to Statistical Learning 2. Performance Evaluation of Learning Models 3. Bayesian Learning 4. Bayes Rule and Cost-Sensitive Learning 5. k-Nearest Neighbors (k-NN) 6. Support Vector Machines 7. Decision Trees and Random Forests 8. 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 and Python languages.
Assessment System
  • % end-of-term-examination/test 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70

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
  • Gareth, J., Witten, D., Hastie, T., Tibshirani, R., and Taylor, J.. An Introduction to Statistical Learning: with Applications in Python. Springer. 2023
  • 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