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Presentation
Presentation
This course consists of an introduction to artificial intelligence for networks and telecommunications. To this end, both classical informed and uninformed search techniques in graphs and trees will be addressed, as well as local search metaheuristics in complex environments.
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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
2 | Mandatory | Português
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Code
Code
ULHT2531-26019
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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
PC1. Origins and History of AI; PC2. Cellular Automata and Neural Networks; PC3. Uninformed search: Breadth-First Search, Depth-First Search, Dijkstra; PC4. Informed search Greedy, Dijkstra, Best-First Search e A*; PC5. Metaheuristics: Simulated Annealing and Tabu Search; PC6. Genetic Algorithms; PC7. Ant Colony Optimization; PC8. Ethics and AI.
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Objectives
Objectives
Learn about the history of Artificial Intelligence and some of the pioneering concepts, like cellular automata and perceptron. Understand the mechanisms of classical informed and uninformed search algorithms. Implement and understand the fundamental mechanisms of some collective intelligence algorithms, such as Ant Colony Optimization, particularly in graph search problems. Notions of ethics in AI.
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Teaching methodologies and assessment
Teaching methodologies and assessment
À excepção do auxílio de ferramentas digitais, o método de ensino será baseado em metodologias tradicionais.
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References
References
Russell, S., & Norvig, P. (2020). Artificial Intelligence: a Modern Approach. 4th edition. http://aima.cs.berkeley.edu
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Office Hours
Office Hours
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Mobility
Mobility
No
