ISLA Santarém 2129

Artificial Intelligence

• ApresentaçãoPresentation

      
• ProgramaProgramme

  Introduction to Artificial Intelligence and its applications Intelligent agents and logical agents Knowledge Representation, Reasoning, and Logic Structures and Objects Knowledge-Based Agents Representation, Reasoning, and Logic  Transforming Knowledge into Action  Propositional, Predicate, Modal, and Temporal Logic  Introduction to Logic Programming  Problem-Solving Methods  Search Agents  Problem Formulation  Informed and Uninformed Search  Evolutionary Computation  Constraint Satisfaction Problems  Problems Considering Adversaries  Modern Heuristics  Machine Learning Classification and Categorization  Inductive Learning  Neural Networks  Data Science  Deep Learning 
• ObjectivosObjectives

  Study the main areas of Artificial Intelligence: Intelligent agents, Search, Problem-solving methods, Heuristics and meta-heuristics, Knowledge Representation and Reasoning, and Machine Learning. Skills: Identify problems that can be solved with Artificial Intelligence; Represent knowledge with computational structures; Programming in logic; Understand and apply the main problem-solving algorithms automatically; Apply Machine Learning techniques; Implement the main algorithms in C#; Use Python AI libraries.
• BibliografiaBibliography

  Aggarwal, C. C. (2021). Artificial Intelligence A Textbook. Springer. Chopra, D., & Khurana, R. (2023). Introduction to Machine Learning with Python. Bentham Science Publishers. Miller, B. N., & Ranum, D. L. (2023). Problem solving with algorithms and data structures using Python (4th ed.). Franklin, Beedle & Associates. Russell, S., & Norvig, P. (2021). Artificial intelligence: a modern approach. Pearson. Teoh, T. T., & Rong, Z. (2022). Artificial Intelligence with Python. Springer Singapore.
• MetodologiaMethodology

  The teaching methodology involves the exposure of each topic of content, with practical application immediately through exercises and work, since this course is essentially laboratory practice. Therefore, the Problem Based Learning (ABRP) methodology will be used.
• LínguaLanguage

  Português
• TipoType

  Semestral
• ECTS

  6
• NaturezaNature

  Mandatory
• EstágioInternship

  Não
• AvaliaçãoEvaluation

  Avaliação contínua:

  • Trabalho prático (Relatório e projeto); 60%;
  • Teste final prático; 40%.

  Avaliação Final:

  Todos os estudantes que não tenham concluído com sucesso a avaliação continua podem realizar um exame final teórico-prático (100%) na época de avaliação definida pela instituição.