Checking date: 28/04/2022


Course: 2022/2023

Statistical Learning
(16487)
Study: Dual Bachelor in Data Science and Engineering and Telecommunication Technologies Engineering (371)


Coordinating teacher: NOGALES MARTIN, FCO. JAVIER

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Linear algebra Probability and Data Analysis Introduction to Statistical Modeling
Objectives
CB2: That students know how to apply their knowledge to their work or vocation in a professional manner and possess the skills that are usually demonstrated through the elaboration and defense of arguments and the resolution of problems within their area of ¿¿study. CB3: That students have the ability to gather and interpret relevant data (usually within their area of ¿¿study) to make judgments that include a reflection on relevant issues of social, scientific or ethical nature. CE3: Ability to correctly identify classification problems corresponding to certain objectives and data and to use the basic results of the multivariate analysis as a basic foundation of the methods of classification, clustering and reduction of the dimension. CG1: Knowledge and adequate skills to analyze and synthesize basic problems related to engineering and data science, solve them and communicate them efficiently. CG4: Ability to solve technological, computer, mathematical and statistical problems that may arise in engineering and data science. CG5: Ability to solve mathematically formulated problems applied to diverse subjects, using numerical algorithms and computational techniques. CG6: Ability to synthesize the conclusions obtained from the analyzes carried out and present them clearly and convincingly, both in writing and orally. RA1: Have acquired advanced knowledge and demonstrated an understanding of the theoretical and practical aspects and work methodology in the field of science and data engineering with a depth that reaches the forefront of knowledge. RA2: Power, through arguments or procedures developed and supported by them, apply their knowledge, understanding of these and their abilities to solve problems in complex or professional and specialized work environments 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, when necessary and relevant, reflection on social, scientific or ethical issues in the field of their field of study. RA4: Being able to cope with complex situations or requiring the development of new solutions in the academic, work or professional field within their field of study. RA5: Know how to communicate clearly and accurately to all types of audiences (specialized or not), 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 to organize their own learning with a high degree of autonomy in all types of contexts (structured or not).
Skills and learning outcomes
Description of contents: programme
1. Introducción al aprendizaje estadístico 2. Evaluación de métodos de aprendizaje 3. Aprendizaje no supervisado 3a. Clustering 3b. Reducción de dimensión 4. Aprendizaje probabilístico 4a. Clasificación estadística 4b. Regresión y predicción 5. Casos de estudio
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
Basic Bibliography
  • 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.


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