ISLA IPGT 2129

Artificial Intelligence

• ApresentaçãoPresentation

  The Artificial Intelligence (AI) course (6 ECTS) introduces students to the fundamental concepts of AI and Machine Learning, placing them in the context of Data Science. Throughout the course, the following topics are covered: types of AI problems, enabling technologies, the ML learning process, as well as supervised and unsupervised algorithms and their practical application with appropriate tools. The methodology combines lectures with demonstrations and practical exercises. Continuous assessment includes one written test (50%) and one practical assignment with a report and presentation (50%), with the option of a final exam at the appropriate time.  
• ProgramaProgramme

  1.Introduction to Artificial Intelligence: motivation, benefits and the type of problems you want to solve. 2.Artificial Intelligence in Data Science. 3.Artificial Intelligence - Technologies that enable the operation. 4.Machine Learning – Learning Process. 5.Types of Machine Learning. 6.Machine Learning – Typologies of Algorithms.    6.1.Supervised Algorithms.    6.2.Unsupervised algorithms. 7. Development and implementation of machine learning algorithms.
• ObjectivosObjectives

  It is intended to transmit to students the principles and characteristics of Artificial Intelligence and respectively Machine Learning, highlighting Search, Knowledge Representation and Reasoning, Planning and Machine Learning. The concept of Artificial Intelligence with Machine Learning is introduced. The essential foundations of artificial intelligence in the domains of machine learning and data science. Machine Learning: supervised algorithms and unsupervised algorithms.
• BibliografiaBibliography

  Oliveira, A. (2019). Inteligência Artificial. Ensaios da Fundação, Edição 2019, ISBN: 9789898943309, Fundação Francisco Manuel dos Santos. Costa, E., Simões, A. (2008). Inteligência Artificial – Fundamentos e Aplicações, 2ª Ed. At. e Aum., Edição 2008, ISBN: 978-972-722-340-4, Editora: FCA. Russell R. & Norvig P. (2010) Artificial Intelligence: A Modern Approach. Third Edition, Prentice Hall. Nilsson, N. J. (2014). Principles of artificial intelligence. Morgan Kaufmann. Mitchell, M. (1998). An introduction to genetic algorithms. MIT press, 1998. Michalewicz, Z. (1996). Genetic Algorithms + data Structures = Evolution Programs, 3rd edition, Springer Verlag,  ISBN 3540606769, 1996.
• MetodologiaMethodology

  The Artificial Intelligence CU will use active, student-centred methodologies, with an emphasis on project-based learning applied to real datasets and the use of open source tools. Flipped classroom logic, collaborative work in pairs/groups and the use of digital platforms (Moodle, forums) will be combined to provide frequent feedback and develop autonomy and critical thinking.
• LínguaLanguage

  Português
• TipoType

  Semestral
• ECTS

  6
• NaturezaNature

  Mandatory
• EstágioInternship

  Não
• AvaliaçãoEvaluation

  Instrumentos de avaliação:

  Descrição	Data limite	Ponderação
  Teste de avaliação	08-01-2026	50%
  Trabalho prático	11-12-2025	50%