- Details
- Category: Discipline
- 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.
- Web Systems and Technology Engineering
- 6159
- 2129
- Artificial Intelligence
- ISLA Santarém6159-2129
- 1
- 6
- 0
- 25
- Não
- Português
- 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.
- Mandatory
- 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.
- 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
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.
- Semestral
- 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.
- Details
- Category: Discipline
- Punch and Enbody R. (2021). The Practice of Computing using Python, 3rd. Ed., Pearson. Carvalho, A. (2021). Práticas de Python - algoritmia e programação. FCA. Guttag, J. V. (2021). Introduction to computation and programming using Python. 3rd edition. MIT Press. Udayan Das et. al (2024).Introduction to Python Programming. Openstax
- Information Systems Technology and Programming
- 2045
- 15888
- Computer Programming II
- ISLA Santarém2045-15888
- 1
- 6
- 0
- 25
- Não
- Português
- Teaching methodologies are based on expository, demonstrative, interrogative, and active methods, in order to promote guided discussion, experimentation, and the resolution of theoretical and practical exercises in the classroom context. Sessions take place in computer labs equipped with all the necessary resources for students to attend classes. Throughout the course, active methodologies such as Problem-Based Learning, Project-Based Learning, and Flipped Classroom will be integrated when appropriate to the progress of the class and the context of the students, with an emphasis on guided problem solving, applied practical work, and the use of face-to-face time for clarification, validation of solutions, and formative feedback.
- Mandatory
Em época normal, a avaliação curricular é composta pela realização de um portfólio de trabalhos realizados em aula, com o peso de 30%, complementa-se a avaliação com uma prova escrita sobre os conhecimentos adquiridos, com um peso de 40% e junta-se um trabalho de grupo com o peso de 30%.
Na época normal, a frequência e o trabalho de grupo são elementos de avaliação obrigatórios. O portfólio e a frequência são realizados presencialmente, a apresentação e defesa do trabalho de grupo decorrem igualmente em sessão presencial. Em caso de não comparência, os alunos terão 0 valores nas respetivas avaliações. O trabalho de grupo é desenvolvido fora da sala de aula, podendo ser dedicado tempo de aula, de forma pontual, à orientação e esclarecimento de dúvidas.
Na época final, de recurso e especial, a avaliação será orientada pelos mesmos objetivos e baseia-se num exame (100%), integrando a componente teórica e a componente prática da unidade curricular.
- Semestral
- Details
- Category: Discipline
- Damjanov, I. (2017). Pathology for the health professions. Elsevier. Goodman, C. C., & Fuller, K. S. (2015). Pathology. Elsevier. Kumar, V., Abbas, A. & Aster, J. (2016). Robbins & Cotran Patologia: bases patológicas das doenças (9th ed.). Rio de Janeiro: Elsevier B. V. Pádua, M. M. (2009). Patologia clínica para técnicos. Lusociência.
- Data and Technology Management in Health
- 7055
- 19278
- General Pathology
- ISLA Santarém7055-19278
- 1
- 4
- 0
- 25
- Não
- Português
- Synchronous (distance): MET 1. - (Support via Zoom + Moodle platforms): Expository, interrogative, and interactive methods: Presentation/explanation of concepts using expository, interrogative, and interactive methods. All pedagogical support materials are made available through the Moodle platform. In-person: MET 2. - Active methodologies: Practical application through exercises and assignments in a classroom setting. Autonomous: MET 3. - Reading of the recommended bibliography for each session. The instructor provides feedback (Tutorial Guidance - OT) on the results obtained by the student in solving exercises and assignments, either in person during classroom sessions or asynchronously through the Moodle platform.
- Mandatory
- O1. Understand the fundamental principles of pathology and their application in health data management. O2. Identify and describe the basic pathological processes that affect different organ systems. O3. Recognize the main pathological characteristics of the most common diseases and their relationship with clinical and technological data.
- 1. Introduction to General Pathology. 2. Cellular and Tissue Changes. 3. Diseases of the Immune System and Neoplasms. 4. Genetic and Pediatric Diseases. 5. Environmental, Nutritional, and Infectious Diseases. 6. Diseases of the Cardiovascular, Hematopoietic, and Lymphatic Systems. 7. Diseases of the Respiratory and Urinary Systems. 8. Diseases of the Digestive and Endocrine Systems. 9. Diseases of the Male and Female Genital Systems, Lower Urinary Tract, and Breast. 10. Diseases of the Bones, Joints, and Muscles. 11. Diseases of the Nervous System.
Avaliação curricular contínua
AVAL 1. - Teste final teórico/prático. AVAL 2. - Teste final teórico.
A classificação final é calculada através da fórmula Classificação Final = 0,5*AVAL 1+0,5*AVAL 2. O estudante é aprovado se obtiver classificação igual ou superior a 9,5 valores.
Época Normal (presencial) - A: O estudante realiza o exame teórico-prático (100%) e é aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.
Época de Recurso (presencial) - A: O estudante realiza o exame teórico-prático (100%) e fica aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.- Semestral
- Details
- Category: Discipline
- Marketing
- 7153
- 7032
- Introduction to Marketing
- ISLA Santarém7153-7032
- 1
- 7
- 0
- 25
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Apple Inc. (2024). Apple Developer Documentation. https://developer.apple.com/documentation Google LLC. (2024). Android Developers: Official Documentation and SDK. https://developer.android.com Griffiths, D., & Griffiths, D. (2024). Head First Android Development (3rd ed.): A Brain-Friendly Guide. O’Reilly Media. Payload Media, Inc. (2024). Android Studio Koala Essentials - Java Edition: Developing Android Apps using Android Studio 2024.1.2 and Java. Payload Media. Smyth, N. (2024). iOS 17 App Development Essentials: Develop iOS apps using Xcode 15, Swift 6 and SwiftUI. The Pragmatic Programmers. Sommerhoff, Peter (2024). Kotlin for android app development. Pearson Education (US)
- Web Systems and Technology Engineering
- 6159
- 16947
- Mobile Application Development
- ISLA Santarém6159-16947
- 1
- 6
- 0
- 25
- Não
- Português
- The teaching methodology uses the exposure of each topic of the contents, then the practical application through exercises and works, since this course is essentially laboratory practice using mainly the methodology Problem Based Learning (PBL).
- Mandatory
- Present the concepts associated with mobile application development, focusing on native applications for Android and IOS with native technologies; Provide knowledge that enables native cross-platform development as well as mobile and hybrid web applications. At the end of the curricular unit students should be able to: Know the main mobile development environments; Characterize and distinguish mobile, hybrid and native web applications; Design and develop native Android apps; Develop native iOS applications; Use frameworks for multiplatform native development; Develop mobile and hybrid web applications using web technologies; - Publish a mobile app.
- 1. Introduction to mobile development 2. Development in the Android environment 2.1. Native application development 2.2. Applications with databases and other types of persistence 2.3. Applications using the internet, web, and online data/web services 2.4. Applications with multimedia and graphics. 2.5. Applications with maps and location. 3. Development in the iOS environment 3.1. Native development 3.2. Applications with databases and online data/web services 4. Native multi-platform development 5. Mobile and hybrid web applications
Avaliação contínua:
- Trabalho prático (Relatório (20%) e projeto (80%).
Avaliação final:
Todos os estudantes que não tenham concluído com sucesso a avaliação contínua podem realizar um exame final prático (100%) na época de avaliação definida pela instituição.
- Semestral
- It covers native and hybrid app development for different platforms. Objectives include creating responsive interfaces, integrating with APIs, and optimizing mobile performance through practical projects and continuous assessment. This training equips students for the modern, mobile-first app ecosystem.
