Artificial Intelligence in Health
- Details
- Category: Discipline
- Aggarwal, C. C. (2021). Artificial Intelligence A Textbook. Springer. Russell, S., & Norvig, P. (2021). Artificial intelligence: a modern approach. Pearson. Teik Toe Teoh, Zheng Rong (2022). Artificial Intelligence with Python. Springer. Chopra, D., & Khurana, R. (2023). Introduction to Machine Learning with Python. Bentham Science Publishers.
- Data and Technology Management in Health
- 7055
- 26700
- Artificial Intelligence in Health
- ISLA Santarém7055-26700
- 3
- 5
- 1258
- 25
- Não
- Português
- Synchronous (distance): Expository method: Presentation of each syllabus topic. Practical application through exercises to consolidate knowledge. In-person: 3. Laboratory practice: Based on the Problem-Based Learning (PBL) methodology aimed at finding solutions to problems identified by students or proposed by the instructor. Autonomous: 4. Guided research proposed by the instructor. The instructor provides feedback on the development of the problem addressed in laboratory practice (Tutorial Guidance - OT), either in person in the classroom or through the Moodle teaching/learning platform.
- Optional
- O1. Present the fundamental concepts of AI. O2. Provide a solid foundational understanding of artificial intelligence, including fundamentals, techniques, and applications in health. O3. Introduce large language models/neural networks. Competencies: C1. Identify health problems that can be solved using artificial intelligence. C2. Represent knowledge with computational structures. C3. Understand and apply the main problem-solving algorithms in health contexts. C4. Understand the challenges associated with machine learning in health and apply appropriate resolution techniques. C5. Use tools and systems based on generative AI.
- 1.Introduction to Artificial Intelligence: Definition and history of AI. Applications and impacts of AI in health. 2.Intelligent Agents: Agents and environments. Simple reactive agents. Goal-based agents. 3.Problem Solving: Problem formulation. Uninformed search strategies. Informed search strategies. 4.Propositional and First-Order Logic: Knowledge representation using logic. Logical inference. Solving health-related problems using logic. 5.Machine Learning: Introduction to supervised and unsupervised learning. Machine learning algorithms applied to health. 6.Ethics and Social Implications of AI in Health. 7.Large Language Models/Neural Networks. 8.Generative Artificial Intelligence.
Avaliação Curricular (contínua):
- A1. Trabalho prático (grupo).
- A2. Teste final teórico-prático (individual).
A classificação final é calculada através da fórmula Classificação Final = A1*0,6+A2*0,4. O estudante é aprovado se obtiver classificação igual ou superior a 9,5 valores.
Avaliação Final (A): O estudante realiza o exame teórico-prático (A=100%) e é aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.
Avaliação em Época de Recurso e Época Especial (A): O estudante realiza o exame teórico-prático (A=100%) e fica aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.- Semestral
