This course will explain the basics of intelligent systems. These methods include mathematical foundations, algorithmic and statistical. The course is divided into three parts, covering the three "legs" basic intelligent systems:
- Knowledge representation and reasoning in environments with automatic certainty of
- knowledge representation and reasoning with uncertainty
- Machine-Learning System
Person in charge
Francisco Javier Larrosa Bondia (
Emma Rollón Rico (
Luis Antonio Belanche Muñoz (
Marta Arias Vicente (
Technical Competences of each Specialization
CTE1 - Capability to model, design, define the architecture, implement, manage, operate, administrate and maintain applications, networks, systems, services and computer contents.
CTE7 - Capability to understand and to apply advanced knowledge of high performance computing and numerical or computational methods to engineering problems.
CTE9 - Capability to apply mathematical, statistical and artificial intelligence methods to model, design and develop applications, services, intelligent systems and knowledge-based systems.
CTR6 - Capacity for critical, logical and mathematical reasoning. Capability to solve problems in their area of study. Capacity for abstraction: the capability to create and use models that reflect real situations. Capability to design and implement simple experiments, and analyze and interpret their results. Capacity for analysis, synthesis and evaluation.
CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
Get languages for modeling and solving problems and reasoning saver and apply them to specific problems with certainty and uncertainty, using specialized tools, while being aware of the implications of complexity theory.
Knowledge representation and reasoning in the context of automatic certainty
Will be propositional logic. Its syntax and semantics, the basic inference algorithm and its expressive power.
Knowledge representation and automatic reasoning with uncertainty
Will be the Bayesian Networks, syntax, semantics, the basic inference algorithms and their expressive power.
Will be the most important machine learning algorithms understanding the strengths and weaknesses of each in order to know what is the most appropriate for each situation
Development of the first theme of the course (propositional logic)
Assimilate the basics of propositional logic (syntax, semantics, inference) understand the expressive power of propositional logic and see examples of actual use.
It combines lectures to introduce the fundamental concepts, the classes of problems to practice and exercise their implications with laboratory classes, where you will see a more practical all this through case study and using packages already implemented.
The course is divided into 3 parts. Each part is evaluated with an exam and a project. All evaluations are worth 1/6 of the final grade.