Checking date:
19/05/2022 17:06:28
Versión en español
Course: 2022/2023
Statistical Learning
(16487)
Bachelor in Data Science and Engineering (Study Plan 2018) (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:
2º
Semester:
1º
Requirements (Subjects that are assumed to be known)
Linear algebra Probability and Data Analysis Introduction to Statistical Modeling
Learning Outcomes
Link to document
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/test 50
% of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
The assessment will be made by weighting the continuous evaluation (50%) and the final exam (50%), with a minimum grade of 5 points over 10 in each assessment activity.
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.
More information:
Aula Global
