Relevance: Robotics and Internet of Things (IoT) are changing more and more all areas of life. Automation and real-time data exchange enable smart solutions in healthcare, manufacturing, logistics and many other areas by increasing efficiency, precision and interconnectivity. However, these technologies also pose significant risks, such as privacy invasion, security threats, and potential job loss. To harness the transformative potential of Robotics and IoT, it is therefore crucial to understand their risks and to practice a responsible and ethical approach.
Objective: the course aims to provide a comprehensive understanding of IoT and Robotics, their different fields of applications, ethical implications, technical foundation and basic skills to design and model IoT and Robotics solutions.
Learning outcome: By the end of the course, students will have acquired sufficient knowledge of Robotics and the Internet of Things, enabling them to use these key technologies of the 21st century to solve challenging problems.
Relevance: Digital education is crucial in today's technological landscape, providing unmatched access to information and learning. It crosses geographical boundaries, allowing learners to acquire knowledge and skills anytime, anywhere. By using digital tools, it enhances engagement, collaboration, and personalized learning. It also equips students with essential digital literacy skills, fostering innovation and adaptability. Additionally, digital education supports continuous professional development, lifelong learning, and makes education more inclusive and accessible to all.
The aim of the course is to develop a comprehensive understanding of digital education, covering its benefits, challenges, and applicable learning theories. Students will learn online learning strategies, pedagogical approaches, and explore various digital tools. The course also focuses on assessing and evaluating online learning programs.
Learning outcome: by the end of the course, students should have a thorough grasp of digital education and its components.
Relevance: Information technologies with growing amounts of digital storage and more devices or sensors than ever before have resulted in massive quantities of diverse data, where applying this data for many useful purposes becomes challenging. Term Big Data indicates massive and often unstructured data, for which traditional data management and analysis tools are insufficient.
The aim of the course is to give an an overview of the Big Data concept and main techniques for working with it effectively. Practical focus is on extracting value and formulating data-driven insights using analytics and visualization.
Learning outcome: by the end of the course, students should have a sufficient knowledge of big data analytics as a tool for addressing research questions and approaching challenging problems with data-driven solutions.
Relevance: the growing digitization of society is a reality that we have been witnessing, with the adoption of products and technologies that have transformed our personal lives, revolutionizing our relationship with information and communication. At the organization level, digital transformation is also motivated by the dissemination of several innovative technologies, potentially transforming business. This course addresses the main aspects of Artificial Intelligence (AI) and modern Machine Learning (ML) techniques, with a perspective of the impact on modern organizations, contextualizing them in business and organizational scenarios of digital transformation.
The aim: delimiting and defining the concepts of intelligence, AI and ML, followed by an overview of large areas within AI. Problem solving techniques are explored: for decision, search and optimization problems. Knowledge representation, as a key aspect, along with reasoning and uncertainty, is introduced, focusing on up-to-date methods. Besides the fundamental concepts of AI, studied since the 60s, recent developments in machine learning/deep learning and natural language processing (NLP) are introduced, by showing and experimenting with computational systems that are becoming increasingly available.
Learning outcome: by the end of the course learners will be able to define what is artificial intelligence and main concepts and questions, analyze an intelligent agent, specifying its performance, analyzing environment characteristics and the characterize the different types of agents, understand and use of machine learning techniques, such decisions trees, neural networks and k nearest neighborhoods, with real data, apply NLP models through available libraries
