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Presentation
Presentation
This course unit aims to provide introductory skills in the field of Artificial Intelligence, equipping students with solid and structured knowledge that enables them to understand theoretical concepts and develop code to solve practical AI problems.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 5
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Year | Nature | Language
Year | Nature | Language
3 | Optional | Português
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Code
Code
ULHT6638-2129
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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
AI basics for some domains. Path search, orientation and navigation: graphs, Dijkstra and A * algorithms, navigation grids and meshes, cost functions. Decisions: decision trees, state machines, behavior trees, other approaches. Learning: Basics, action prediction, Naive Bayes Classifiers, other approaches. Board games: basics, family of minimax algorithms, MCTS, other approaches.
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Objectives
Objectives
Understanding the basics of Artificial Intelligence. Understanding of basic and intermediate concepts of intelligent movement and wayfinding. Understanding of intermediate and advanced concepts in decision making through state machines, behavior trees, among others. Understanding of Artificial Intelligence topics in board games. Ability to solve problems involving the concepts acquired, both at an abstract level and at a practical level (programming).
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Teaching methodologies
Teaching methodologies
Lecturing consists of theoretical and practical classes. The theoretical component is essentially expository, the theory being presented together with concrete examples. In the practical component, practical programming problems related to the theory taught are developed and solved. In this course unit the evaluation includes the following elements: Theoretical assessment, in the form of written test, exercises, with a weight of 50% in the final grade (minimum grade: 9.5 points). Practical assessment (projects / programming problems / presentations), with a weight of 50% in the final grade (minimum grade: 9.5 points)
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References
References
Russel, Stuart; Norvig, Peter: Inteligência Artificial: Uma abordagem Moderna. Tradução da 3ª. Campus Editora. 2013 ,Melanie Mitchell Artificial Intelligence: A Guide for Thinking Humans, Pelikan Book, 2019
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Assessment
Assessment
Época normal (avaliação contínua)
- Componente teórica: 2 frequências correspondendo a 50% (25% + 25%) da nota (nota mínima: 9.5 valores na média ponderada).
- Componente prática: assiduidade e participação nas aulas (50%) + projecto final 50% (nota mínima: 9.5 valores na média ponderada).
- A frequência das aulas é obrigatória de acordo com o artigo 11º do Regulamento Geral de Avaliação da Universidade Lusófona.
Época de recurso
- Exame teórico (50% da nota final, nota mínima: 9.5 valores)
- Projeto prático (50% da nota final, nota mínima: 9.5 valores)
Época especial
- Exame teórico (50% da nota final, nota mínima: 9.5 valores)
- Projeto prático (50% da nota final, nota mínima 9.5 valores)
- A avaliação de época especial está disponível de acordo com os regulamentos da Universidade Lusófona.
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Mobility
Mobility
No
