Competencies and objectives

 

Course context for academic year 2025-26

The course is located in the first semester of the Master's Degree in Artificial Intelligence and introduces the student to the use of artificial vision techniques, with the aim of studying, through case studies, the nature or optimal vision systems in engineering, supported by information and communication technologies.

 

 

Course competencies (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2025-26

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.
  • 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.
  • CG9 : Know how to manage projects related to artificial intelligence, complying with current regulations and ensuring the quality of the service.

 

Specific Competences

  • CE06 : Know and apply image processing tools and techniques suitable for solving computer vision problems.
  • CE07 : Know and apply tools and techniques for the extraction of visual information suitable for the resolution of artificial vision problems.

 

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)

- To understand how an artificial vision system works.

- To understand the different methods and techniques used in artificial vision to extract information from an image.

- Know the processing stages into which a machine vision system is usually broken down.

- Acquire the ability to apply artificial vision techniques to solve real-world problems.

 

 

Specific objectives stated by the academic staff for academic year 2025-26

Among the training objectives of this course we highlight:

- To understand the operation of a computer vision system.

- To know the different methods and techniques used in computer vision for the extraction of information from an image.

- To know the processing stages in which a computer vision system is usually decomposed.

- To know how to define artificial intelligence (AI) schemes based on computer vision for real-world applications.

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

- Acquire the ability to apply computer vision techniques to solve real-world problems.

- Introduction to image processing with machine learning.

 

 

General

Code: 43503
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: LANGUAGES AND COMPUTING SYSTEMS
    Area: LANGUAGES AND COMPUTING SYSTEMS
    Theoretical credits: 0,3
    Practical credits: 0,3
  • Dept: SCIENCE OF COMPUTING AND ARTIFICIAL INTELLIGENCE
    Area: CIENCIA DE LA COMPUTACIO, INTEL·LIGENCIA ARTIFICIA
    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