Digital Transformation and Emerging Technologies

The classes for this curricular unit are of a theoretical-practical nature.
The syllabus content is taught through the presentation of theory, followed by demonstrations of its application through practical examples,
and finally, students solve exercises on the topics covered.
For this purpose, slides, demonstration videos, and practical exercises are used.
The exercises completed in class form the foundation for solving future projects.
Group work, both in class and in assigned tasks, is strongly encouraged, aiming to promote coordination and cooperation skills.
When presenting any topic of the syllabus, the expository methodology is initially used, followed by the interrogative methodology, where
students are invited to ask and answer questions about the content covered.
In a later phase, for the syllabus topics in points 2 and 3, students will have to solve practical exercises. In these cases, the active learning
methodology is prioritized, with an emphasis on independent problem-solving, complemented by knowledge sharing among students.

This curricular unit aims to provide students with a set of knowledge about the concepts and technologies that support organizational
transformations, focusing on the integration and automation of organizational processes.
Students are expected to acquire the fundamental skills that enable them to:
O1 – Understand the concepts of Digital Transformation and Industry 4.0 and their implications for organizations;
O2 – Become familiar with the most common Machine Learning (ML) techniques and some of their main applications;
O3 – Apply ML techniques, based on Neural Networks, to develop solutions;
O4 – Understand what the Internet of Things (IoT) and Edge Computing are, as well as their application domains;
O5 – Develop solutions that integrate IoT and ML technologies together;
O6 – Recognize the challenges posed by these technologies, particularly ethical issues and resource consumption.

1. Fundamentals of Digital Transformation
2. Artificial Intelligence (AI) and Machine Learning (ML)
2.1 Introduction to AI and ML
2.2 Commonly Used Supervised and Unsupervised Techniques
2.3 Building Models Based on Neural Networks
2.4 Model Evaluation and Tuning
2.5 Introduction to Deep Learning
3. Internet of Things (IoT) and Edge Computing
3.1 Motivation, Basic Concepts, and Application Domains
3.2 Programming Foundations for IoT Devices
3.3 Key Communication Technologies and Application Protocols
3.4 Developing Applications Incorporating Edge Computing
4. Future Trends and Ethics in Emerging Technologies

NAO