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Presentation
Presentation
This course provides the the contact to computational agents with rational behaviours that make use of paradigms and structured data models, ultimately supporting decision theories. The technics developed in this course apply to a variety of problems related to artificial intelligence, being the foundations to the development of various application fields.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 6
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Year | Nature | Language
Year | Nature | Language
3 | Mandatory | Português
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Code
Code
ULP452-22525
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
Concepts Search Trees and graphs Uninformed search Informed Search A* Stochastic Search Genetic Algotithms Constraint Satisfaction Problems Reinforced Learning Machine Learning Redes Neuronais Árvores de decisão
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Objectives
Objectives
This course provides general knowledge about ideas and techniques underlying the design of rational computation systems. Students engaging on this course will understand the construction of autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial scenarios. The agents will infer data in environments of uncertainty and optimize the output actions based on reward structures. Students will develop knowledge on classification algorithms based on neural networks and machine learning.
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Teaching methodologies
Teaching methodologies
Theoretic classes are expository, always covering practical examples on the covered topics, in a way to provide full understanding of the topics. Practical classes enable the student to exercise and test the topics.
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References
References
Ernesto Costa e Anabela Simões; Inteligência Artificial: Fundamentos e Aplicações; FCA - Editora de Informática
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Assessment
Assessment
Avaliação contínua:
Descrição
Data limite
Ponderação
Participação / Assiduidade 10% Trabalhos práticos durante as aulas
20%
Teste Escrito Teórico-Prático 1
30%
Teste Escrito Teórico-Prático 2
40%
Avaliação Recurso e Especial: Exame Escrito (100%)
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Mobility
Mobility
No
