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Course: 2025/2026

Regression in High Dimension

(20376)

Bachelor in data and business analytics (Plan: 560 - Estudio: 203)

Coordinating teacher: NOGALES MARTIN, FRANCISCO JAVIER

Department assigned to the subject: Statistics Department

Type: Electives

ECTS Credits: 6.0 ECTS

Course: 4º

Semester:

Description of contents: programme

1. Introduction: explain or predict? 2. Statistical tools: selection of models 3. Statistical tools: regularization methods 4. Statistical tools: selection of functions. 5. Machine Learning Tools: Closest Neighbors 6. Machine Learning Tools: Vector Regression Support 7. Machine Learning Tools: Regression and Related Trees 8. Machine Learning Tools: Neural Networks

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

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 (both the continuous ev. and the final exam).

Extraordinary call: regulations

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

More information: Aula Global.