Checking date: 09/04/2025 09:52:58


Course: 2025/2026

Regression in High Dimension
(17310)
Dual Bachelor Data Science and Engineering - Telecommunication Technologies Engineering (Study Plan 2020) (Plan: 456 - Estudio: 371)


Coordinating teacher: NOGALES MARTIN, FRANCISCO JAVIER

Department assigned to the subject: Statistics Department

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Basic knowledge of mathematics and statistics
Objectives
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
Learning Outcomes
LEARNING OUTCOMES RA1:Students should have acquired advanced knowledge and demonstrated an understanding of the theoretical and practical aspects and working methodology in the field of data science and engineering with a depth that reaches the forefront of knowledge. RA2:Be capable of applying their knowledge and problem-solving skills, through arguments or procedures developed and sustained by themselves, in complex or professional and specialized work settings that require the use of creative and innovative ideas RA3:Have the ability to collect and interpret data and information on which to base their conclusions including, where appropriate and pertinent, reflection on issues of a social, scientific or ethical nature within their field of study RA4:Be able to cope with complex situations or those that require the development of new solutions in the academic, work or professional field within their field of study RA5:Know how to communicate to all types of audiences (specialized or not) in a clear and precise manner, knowledge, methodologies, ideas, problems and solutions within the scope of their field of study RA6:Be able to identify their own training needs in their field of study and work or professional environment and organize their own learning with a high degree of autonomy in all types of contexts (structured or not). BASIC COMPETENCES CB3:Students have the ability to gather and interpret relevant data (usually within their field of study) in order to make judgements which include reflection on relevant social, scientific or ethical issues. CB5:Students will have developed the learning skills necessary to undertake further study with a high degree of autonomy. GENERAL COMPETENCES CG4:Ability to solve technological, computer, mathematical and statistical problems that may arise in data engineering and science CG6:Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally SPECIFIC COMPETENCES CE2:Ability to correctly identify predictive problems corresponding to certain objectives and data and to use the basic results of regression analysis as the basis for prediction methods CE13:Ability to apply and design machine learning methods in classification, regression and clustering problems and for supervised, unsupervised and reinforcement learning tasks
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/test 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50

Calendar of Continuous assessment


Extraordinary call: regulations

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


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