Checking date: 27/05/2024


Course: 2024/2025

Statistical Learning
(16487)
Bachelor in Data Science and Engineering (Plan: 392 - Estudio: 350)


Coordinating teacher: NOGALES MARTIN, FRANCISCO JAVIER

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Linear algebra Probability and Data Analysis Introduction to Statistical Modeling
Objectives
Become familiar with different analytical tools, based on data, to make decisions Acquire skills in unsupervised learning to build clusters and decrease dimensionality in big datasets Develop skills for the main statistical and machine-learning tools in supervised learning: classification and regression Use these models to make practical predictions/classifications and perform analytical inferences Handle the R language for these tools
Skills and learning outcomes
Description of contents: programme
1. Introduction to the statistical learning 2. Evaluation of learning methods 3. Unsupervised learning 3a. Clustering 3b. Dimension reduction 4. Statistical classification 5. Case studies
Learning activities and methodology
Theory (3 ECTS), Practice (3 ECTS). 50% lectures with teaching materials available on the Web. The other 50% practical sessions (computer labs).
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
Extraordinary call: regulations
Basic Bibliography
  • BISHOP, C.M.. "PATTERN RECOGNITION AND MACHINE LEARNING". SPRINGER SCIENCE AND BUSINESS MEDIA. 2006
  • FRIEDMAN, J.; HASTIE, T.; TIBSHIRANI, R. . "THE ELEMENTS OF STATISTICAL LEARNIG". NEW YORK, SPRINGER SERIES IN STATISTICS. 2001
  • K. Murphy. Machine Learning, A Probabilistic Perspective. MIT Press. 2012

The course syllabus may change due academic events or other reasons.


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