Checking date: 19/05/2022


Course: 2022/2023

Statistical Learning
(16487)
Study: Bachelor in Data Science and Engineering (350)


Coordinating teacher: NOGALES MARTIN, FCO. JAVIER

Department assigned to the subject: Department of Statistics

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
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. Probabilistic learning 4a. Statistical classification 4b. Regression and prediction 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
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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