Competencies and objectives

 

Course context for academic year 2023-24

El aprendizaje automático (en inglés Machine Learning) se basa en la inferencia a partir de ejemplos para aprender modelos sin ser explícitamente programados con un conjunto fijo de reglas.

El proceso de se divide en una primera fase de aprendizaje donde un modelo aprende patrones y relaciones entre variables a partir de ejemplos (datos) para luego en una segunda fase ser utilizado para realizar predicciones o tomar decisiones sobre nuevos datos.

Existen varios tipos de técnicas como aprendizaje supervisado, no supervisado y por refuerzo. El aprendizaje supervisado implica el uso de datos etiquetados para entrenar el modelo, mientras que el aprendizaje no supervisado se utiliza para encontrar patrones y estructuras en los datos no etiquetados. El aprendizaje por refuerzo implica que el algoritmo aprenda a través de un proceso de ensayo y error, recibiendo recompensas positivas o negativas en función de su comportamiento.

Estás técnicas se aplican en una amplia variedad de campos como la fabricación, las ventas, la salud, los viajes y alojamientos, los servicios financieros y la energía, entre otros. En la actualidad, se van ampliando los campos de aplicación y problemas que son capaces de resolver.

 

 

Course content (verified by ANECA in official undergraduate and Master’s degrees)

Transversal Competences

  • CT1 : Be able to lead projects related to artificial intelligence, as well as to manage work teams.
  • CT2 : Demonstrate computer and information skills in the field of artificial intelligence.
  • CT3 : Demonstrate oral and written communication skills.

 

General Competences

  • CG1 : Apply the knowledge acquired to real problems related to artificial intelligence.
  • CG10 : Be able to use engineering principles and modern computer technologies to research, design and implement new applications of artificial intelligence,
  • CG2 : Be able to develop and learn in a self-directed or autonomous way topics related to artificial intelligence.
  • CG5 : Know how to manage the available information and resources related to artificial intelligence.
  • CG6 : Being able to adapt to environments related to artificial intelligence, fostering teamwork, creativity,
  • CG7 : To be able to adapt to the constant evolution of the discipline and to understand and apply new technical and scientific developments related to artificial intelligence.
  • CG8 : Knowing how to plan, design, develop, implement and maintain products, applications and services related to artificial intelligence, taking into account technical, economic and efficiency aspects.

 

Specific Competences

  • CE08 : In-depth knowledge of machine learning technologies, algorithms and tools (including supervised, unsupervised or reinforced learning).
  • CE09 : Learn to use metrics and techniques for the validation and comparison of the results of machine learning methods.

 

Basic Competences

  • CB10 : Students possess the learning skills that will enable them to continue studying in a way that will be largely self-directed or autonomous. be largely self-directed or autonomous.
  • CB6 : Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context.
  • CB7 : Students should be able to apply their acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their field of study.
  • CB8 : Students are able to integrate knowledge and deal with the complexity of making judgements on the basis of incomplete or limited information, including reflections on the social and ethical responsibilities associated with information which, while incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgements.
  • CB9 : Students are able to communicate their conclusions and the ultimate knowledge and rationale behind them to specialist and non-specialist audiences in a clear and unambiguous way.

 

 

 

Learning outcomes (Training objectives)

- Understand the fundamental concepts of machine learning.

- Understand the phases of data processing, feature selection and evaluation methods of a classification system.

- Describe the main architectures used in machine learning, as well as the most typical applications.

- Identify the most appropriate type of machine learning algorithm for various types of problems in different domains.

- Implement machine learning algorithms using different tools.

 

 

Specific objectives stated by the academic staff for academic year 2023-24

No data

 

 

General

Code: 43504
Lecturer responsible:
RICO JUAN, JUAN RAMON
Credits ECTS: 4,50
Theoretical credits: 0,90
Practical credits: 0,90
Distance-base hours: 2,70

Departments involved

  • Dept: LANGUAGES AND COMPUTING SYSTEMS
    Area: LANGUAGES AND COMPUTING SYSTEMS
    Theoretical credits: 0,9
    Practical credits: 0,9
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught