Competencies and objectives
Course context for academic year 2023-24
El aprendizaje profundo ha supuesto una revolución en las técnicas de inteligencia artificial. Gracias a estos métodos se han podido abordar problemas que hace unos pocos años se consideraban inviables. Esta asignatura se adentra en los conceptos fundamentales del aprendizaje profundo. Se asume que el alumnado ha cursado la asignatura de Técnicas de Aprendizaje Automático y que ha adquirido los conocimientos y competencias que allí se abordan.
Course content (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2023-24
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.
- CG3 : Know how to operate in multidisciplinary and/or international contexts, providing solutions from the point of view of 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).
- CE23 : Ability to design and deploy end-to-end deep learning solutions for applications in different domains.
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)
- Understand the fundamental concepts of deep learning.
- Train a deep neural network by selecting the most appropriate characteristics of the same depending on the type of problem and optimising the hyperparameters.
- Describe the main architectures used in deep learning, as well as the most typical applications.
- Identify the most appropriate type of deep learning algorithm for various types of problems in different domains.
- Implement deep learning algorithms using different tools.
Specific objectives stated by the academic staff for academic year 2023-24
No data
General
Code:
43509
Lecturer responsible:
PERTUSA IBAÑEZ, ANTONIO JORGE
Credits ECTS:
4,50
Theoretical credits:
0,90
Practical credits:
0,90
Distance-base hours:
2,70
Departments involved
-
Dept:
INFORMATION TECHNOLOGY AND COMPUTING
Area: COMPUTER ARCHITECTURE
Theoretical credits: 0,3
Practical credits: 0,3 -
Dept:
LANGUAGES AND COMPUTING SYSTEMS
Area: LANGUAGES AND COMPUTING SYSTEMS
Theoretical credits: 0,6
Practical credits: 0,6
This Dept. is responsible for the course.
This Dept. is responsible for the final mark record.
Study programmes where this course is taught
-
UNIVERSITY MASTER'S DEGREE IN ARTIFICIAL INTELLIGENCE
Course type: COMPULSORY (Year: 1)