Checking date: 28/04/2022

Course: 2022/2023

Regression in High Dimension
Study: Bachelor in Data Science and Engineering (350)

Coordinating teacher: NOGALES MARTIN, FCO. JAVIER

Department assigned to the subject: Statistics Department

Type: Electives
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Basic knowledge of mathematics and statistics
Become familiar with different analytical tools, based on data, to make business decisions Capacity to develop skills to analyze and find relationships between many variables/features Relax some of the assumptions in classical linear regression Deal with the curse of dimensionality in high-dimensional problems Acquire knowledge about the main tools in advanced predictive tools and handle the R language with those models
Skills and learning outcomes
Description of contents: programme
1. Efficient estimation for least-squares 2. Extending Linear Models 3. Statistical-Learning Tools 4. Machine-Learning Tools
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

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

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