Base Knowledge
No specific knowledge is required.
Teaching Methodologies
Lectures will be given by the course instructors or invited guest speakers. Discussions on the course topics are also planned.
Learning Results
• Understand the concepts of intelligence, intelligent systems, and their importance in decision support
• Understand the advantages and limitations of techniques used in new systems and models
• Learn about knowledge extraction processes, their limitations, and risks
• Understand the importance, limitations, and risks of new digital tools in management and communication
Program
1. Artificial intelligence, knowledge discovery, and decision support. Cloud computing, IoT, biometric and non-biometric sensing, Blockchain.
2. Data, learning models, and knowledge acquisition.
3. Big Data and Data Mining.
4. Social networks. Augmented reality. Digital tools and human perception.
5. Classification, detection, and prediction models.
6. Practical cases in healthcare.
Curricular Unit Teachers
Carlos Manuel Jorge da Silva PereiraGrading Methods
The evaluation will be based solely on the completion of a research project, which must be presented and defended.
Internship(s)
NAO
Bibliography
Ryan Light, and James Moody (Eds) (2020), The Oxford Handbook of Social Networks, Oxford University Press
Charles Kadushin (2011), Understanding Social Networks: Theories, Concepts, and Discoveries, Oxford University Press
Stuart Russell, Peter Norvig (2020) Artificial Intelligence: A Modern Approach (Pearson Series in Artificial Intelligence) 4th Edition, Pearson.
Steven L. Brunton (2019). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control 1st Edition. Cambridge University Press.
Binto George, Gail Carmichael, Susan Mathai (2021). Artificial Intelligence Simplified: Understanding Basic Concepts Cstrends LLP; 2nd edition.
Alyssa Simpson Rochwerger, Wilson Pang (2021). Real-World AI: A Practical Guide to Responsible Machine Learning, Lioncrest Publishing.