The aim of this course is to provide the students with the knowledge and skills required to design and implement effective and efficient Computational Intelligence solutions to problems for which a direct solution is impractical or unknown. Specifically, students will acquire the basic concepts of fuzzy, evolutionary and neural computation. The student will also apply this knowledge to solve some real case studies.
Person in charge
Maria Angela Nebot Castells (
Enrique Romero Merino (
Luis Antonio Belanche Muñoz (
René Alquezar Mancho (
Generic Technical Competences
CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.
Technical Competences of each Specialization
CEA4 - Capability to understand the basic operation principles of Computational Intelligence main techniques, and to know how to use in the environment of an intelligent system or service.
CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.
CEP2 - Capability to solve the decision making problems from different organizations, integrating intelligent tools.
CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.
Appropiate attitude towards work
CT5 - Capability to be motivated for professional development, to meet new challenges and for continuous improvement. Capability to work in situations with lack of information.
Know the scope of Computational Intelligence (CI), and the types of tasks that can be tackled with CI methods
Know the most important modern computational intelligence techniques
Organize the problem solving flow for a computational intelligence problem, analyzing the possible options and choosing the most appropriate techniques or combinations of techniques
Decide, defend and criticize a solution to a computational intelligence problem, arguing on the strengths and weaknesses of the chosen approach
Learn the fundamentals of neural computation and apply them effectively to develop correct and efficient solutions to a computational intelligence task
Learn the fundamentals of evolutionary computation and apply them correctly to develop correct and efficient solutions to computational intelligence tasks.
Learn the fundamentals of fuzzy computation and apply them correctly to develop correct and efficient solutions to computational intelligence tasks
The teacher presents the fundamentals of neural computing: inspiration in biological neuron models, architectures and training algorithms. The teacher explains the concepts of learning and generalization and introduces methodologies for obtaining effective models and to guarantee an honest assessment of their effectiveness. Objectives:25 Contents:
The professor explains the fundamentals of evolutionary computation: evolutionary processes in nature, genetic operators, evolutionary optimization algorithms. Focuses on genetic algorithms and Evolution Strategies and CMA-ES. Points to other existing evolutionary algorithms. Objectives:26 Contents:
The teacher presents one or more real case studies that might require solutions from computational intelligence. The teacher looks at the options and outlines one or more possible solutions, discussing their advantages and disadvantages.
The teacher presents the course work that must be carried out, which is similar to previous case studies. Objectives:134 Contents:
The topics exposed in the lectures are very well motivated (why is this important?) and motivating (why is this relevant nowadays?) and supplemented with many real examples. These lectures will introduce all the knowledge, techniques, concepts and results necessary to achieve a solid understanding of the fundamental concepts and techniques.
These concepts are reflected in the practical work that must be delivered at the end of the course. There are three laboratory sessions serve to reinforce the theoretical concepts introduced in the lectures as well as to prepare for the practical work. This practical work requires the student to pick a real problem that collects and integrates the knowledge and skills of the course. There is also a written test of essential knowledge of the subject. In addition, there are 3 small practical exercises after each laboratory class.
Elementary notions of probability, statistics, linear algebra and real analysis
HI HA UN PETIT CANVI, AQUEST QUADRIMESTRE NO S'EXPLICARAN LES XARXES RBF DONCS NO ES DISPOSARÀ D'HORES SUFICIENTS.
THERE IS A SMALL CHANGE IN THE CONTENT OF THE COURSE, THIS QUARTER THE RBF NETWORKS WILL NOT BE EXPLAINED AS THERE WILL NOT BE SUFFICIENT HOURS.
LES 3 CLASSES DE LABORATORI QUE ES FAN AL LLARG DEL CURS ES FARAN ONLINE EN FORMAT ASÍNCRON. LES CLASSES DE TEORIA SEGUIRAN SENT PRESENCIALS, COM SEMPRE.
AS FOR THE TEACHING METHODOLOGY, THE 3 LABORATORY CLASSES THAT ARE HELD THROUGHOUT THE COURSE WILL BE HELD ONLINE IN ASYNCHRONOUS FORMAT. THEORY CLASSES WILL CONTINUE TO BE FACE-TO-FACE, AS ALWAYS.
NO HI HA CANVIS RESPECTE LA INFORMACIÓ PUBLICADA A LA GUIA DOCENT.
THERE ARE NO CHANGES IN THE EVALUATION METHOD REGARDING THE INFORMATION PUBLISHED IN THE TEACHING GUIDE
EN CAS DE SUSPENSIÓ DE L'ACTIVITAT DOCENT PRESENCIAL, LES CLASSES DE TEORIA ES FARAN ONLINE DE MANERA SÍNCRONA I LES DE LABORATORI SEGUIRAN SENT ONLINE EN FORMAT ASÍNCRON.
IN THE EVENT OF SUSPENSION OF THE FACE-TO-FACE TEACHING ACTIVITY, THEORY CLASSES WILL BE DONE ONLINE SYNCHRONOUSLY AND THE LABORATORY CLASSES WILL STILL BE ONLINE IN ASYNCHRONOUS FORMAT.
Where we are
B6 Building Campus Nord
C/Jordi Girona Salgado,1-3
08034 BARCELONA Spain
Tel: (+34) 93 401 70 00