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Presentation
Presentation
The course introduces the fundamental concepts, methods, and techniques of Artificial Intelligence. It covers informed and uninformed search algorithms, an introduction to metaheuristic methods, and a first approach to machine learning through probabilistic classification, namely the Naive Bayes classifier. The course aims to provide conceptual and practical foundations for computational problem solving and for further study in areas such as Artificial Intelligence, Machine Learning, and Optimization.
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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
ULHT6634-24452
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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
1. Introduction to Artificial Intelligence: Definition and history of AI. Applications and societal impacts of AI. 2. Intelligent Agents: Agents and environments. Simple reflex agents. Goal-based agents. 3. Problem Solving: Problem formulation. Uninformed search strategies. Informed search strategies. 4. Introduction to Metaheuristics. 5. Machine Learning: Introduction to supervised and unsupervised learning. Learning algorithms. Naive Bayes classifier. 6. Ethics and Social Implications of AI.
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Objectives
Objectives
The course aims to provide students with a solid understanding of the fundamental concepts, methods, and techniques of Artificial Intelligence. Students should be able to identify problems suitable for AI-based approaches, represent problems and knowledge using appropriate computational structures, understand and apply the main search and problem-solving algorithms, and acquire introductory knowledge of metaheuristic methods and Machine Learning. Particular emphasis is placed on selecting and applying appropriate techniques to solve practical problems.
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Teaching methodologies
Teaching methodologies
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References
References
Aggarwal, C. C. (2021). Artificial Intelligence A Textbook. Springer. Russell, S., & Norvig, P. (2021). Artificial intelligence: a modern approach. Pearson. Teoh, T. T., & Rong, Z. (2022). Artificial Intelligence with Python. Springer. Chopra, D., & Khurana, R. (2023). Introduction to Machine Learning with Python. Bentham Science Publishers.
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Assessment
Assessment
Descrição dos instrumentos de avaliação (individuais e de grupo) ¿ testes, trabalhos práticos, relatórios, projetos... respetivas datas de entrega/apresentação... e ponderação na nota final.
Exemplo:
Descrição
Data limite
Ponderação
Prova de Avaliação de Conhecimentos Escrita (Individual)
08-01-2026
45%
Trabalho Prático (Grupo)
22-01-2026
45%
Atividades Práticas em contexto de Sala de Aula
10%
No momento de avaliação "Atividades Práticas" está previsto a execução dos mesmos predominantemente em aula, com aviso prévio.
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Mobility
Mobility
No
