Competencies and objectives

 

Course context for academic year 2024-25

This is a continuation of subjects related to Artificial Intelligence (AI) seen in the Bachelor's Degree in Computer Engineering or the Bachelor's Degree in Artificial Intelligence, such as Intelligent Systems.
Graduates in the Master's Degree in Artificial Intelligence should know the fundamentals and techniques of AI to address problem solving and projects in which they will be involved during their professional practice.

 

 

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

  • CE01 : Know the fundamentals of heuristic optimisation and apply heuristic algorithms to search problems, including evolutionary search and constraint satisfaction 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)

- Define an efficient search space and cost function from a problem.
- Design and implement an evolutionary search algorithm to solve a problem.
- Formulate a problem as a constraint satisfaction problem and implement it efficiently.
- Define the concept of a planning system and its differences with respect to classical search techniques.

 

 

Specific objectives stated by the academic staff for academic year 2024-25

1. Compare and contrast the most common models for knowledge representation, identifying their strengths and weaknesses.
2. Build knowledge-based systems and engineering solutions to solve them.
3. Explain the difference between monotonic and non-monotonic inference.
4. Define an efficient search space and cost function from a problem.
5. Design and implement an evolutionary search algorithm to solve a problem.
6. Formulate a problem as a constraint satisfaction problem and implement it efficiently.
7. Define the concept of a planning system and its differences with respect to classical search techniques.

 

 

General

Code: 43500
Lecturer responsible:
Escalona Moncholí, Félix
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