ISLA Santarém 2129

Artificial Intelligence

• ApresentaçãoPresentation

  This course explores advanced techniques such as intelligent agents, search algorithms, machine learning, and neural networks. Students apply AI models in real-world scenarios, developing decision support systems with open-source tools. With an emphasis on theoretical and practical classes, it promotes skills for innovation in automation and predictive analytics.
• ProgramaProgramme

  1. Introduction to Artificial Intelligence and its applications 2. Intelligent agents and logical agents 3. Knowledge representation, reasoning, and logic 3.1 Structures and objects 3.2 Knowledge-based agents 3.3 Representation, reasoning, and logic  3.4 Transforming Knowledge into Action  3.5 Propositional, Predicate, Modal, and Temporal Logic  3.6 Introduction to Logic Programming  4. Problem-Solving Methods  4.1 Search Agents 4.2 Problem Formulation 4.3 Informed and Uninformed Search 4.4 Evolutionary Computation 4.5 Constraint Satisfaction Problems 4.6 Problems Considering Adversaries 4.7 Modern Heuristics 5. Machine Learning Classification and Categorization  5.1 Inductive Learning  5.2 Neural Networks  5.3 Data Science  5.4 Deep Learning  6. Implementation of Algorithms Implementation in C# AI Libraries in Python 
• 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.