Checking date: 26/04/2024

Course: 2024/2025

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

Coordinating teacher: DELGADO GOMEZ, DAVID

Department assigned to the subject: Statistics Department

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
1. Principal Component Analysis (PCA) 2. Multivariate Normal Distribution 3. Discriminant Analysis 4. Supervised Learning: k-Nearest Neighbors, Decision Trees, and Random Forests 5. Bias-Variance Tradeoff and Cross-Validation 6. Support Vector Machines (SVM) 7. Unsupervised Learning: K-means and Expectation-Maximization (EM) algorithm for Gaussian Mixture Models
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 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60

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