Competencies and objectives
Course context for academic year 2024-25
La asignatura está ubicada en el primer semestre del Máster Universitario en Inteligencia Artificial e introduce al estudiante en el uso de las técnicas de Razonamiento bajo incertidumbre, con el objetivo de estudiar, a través de casos prácticos, la naturaleza o los sistemas óptimos de razonamiento computacional bajo incertidumbre, en las ingenierías, apoyándose en tecnologías de la información y las comunicaciones.
Course content (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2024-25
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 : Show 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.
- 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
- CE02 : Select and apply the appropriate model to deal with uncertainty in knowledge representation for a given problem. problem.
- CE03 : Design and implement intelligent systems that allow decisions to be made from a context in which there is uncertainty in the input data.
Basic Competences
- CB10 : That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
- CB6 : Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context
- CB7 : That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area 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)
- Know and understand the most common probabilistic reasoning models and probabilistic reasoning models in time.
- Select, design and implement a probabilistic reasoning model to solve a problem.
- Describe the relationship between preferences and utility functions.
- Represent a decision problem by means of a decision network.
- Design and implement a fuzzy expert system.
Specific objectives stated by the academic staff for academic year 2024-25
No data
General
Code:
43502
Lecturer responsible:
Curado Navarro, Manuel
Credits ECTS:
4,50
Theoretical credits:
0,90
Practical credits:
0,90
Distance-base hours:
2,70
Departments involved
-
Dept:
SCIENCE OF COMPUTING AND ARTIFICIAL INTELLIGENCE
Area: CIENCIA DE LA COMPUTACIO, INTEL·LIGENCIA ARTIFICIA
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
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UNIVERSITY MASTER'S DEGREE IN ARTIFICIAL INTELLIGENCE
Course type: COMPULSORY (Year: 1)