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Bachelor in Data Science and Engineering (350)
NOGALES MARTIN, FCO. JAVIER
Department assigned to the subject:
Department of Statistics
Requirements (Subjects that are assumed to be known)
Linear algebra Probability and Data Analysis Introduction to Statistical Modeling
Skills and 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).
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.
% end-of-term-examination 50
% of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
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.