Checking date: 28/05/2025 14:24:09


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:
Semester:




Learning Outcomes
K2: Know basic humanistic contents, oral and written expression, following ethical principles and completing a multidisciplinary training profile. K4: Know the models and methods of statistical analysis for both static and dynamic data K9: now the existing models in cost management and apply them to any production process and the main instruments of accounting management for decision making, with the aim of obtaining an integrated vision of the operational, organizational and behavioral contexts in which accounting information systems are used for senior management. K12: now how to identify or be able to create the statistical or probabilistic model appropriate to the specific problem arising in each business activity (finance, marketing, production planning and control, etc.). C1: Develop and master interpersonal skills on initiative, responsibility, conflict resolution and negotiation, which are essential in the professional environment. C4: Ability to develop and validate statistical models that help to address and solve problems relevant to today's society. C6: Ability to interpret the results of quantitative analysis, prepare clear reports and communicate conclusions effectively, using advanced data analysis tools. C7: Ability to access, analyze and classify large volumes of data of a highly heterogeneous nature (Big Data), as well as to manage and design advanced data analysis and AI tools in social and business applications. S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S5: Manipulate computationally and analytically the established models, taking advantage of the power of statistical methods, optimization, etc., and perform the analysis of the results obtained.
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




Extraordinary call: regulations

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


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