All the courses taught by members of the KnowDive group are about topics which relate to the group main research issues. In a nutshell:

  • Logica Computazionale (Computational Logic - CL). This course is taught in Italian. The intended students are students of the bachelor degree in Computer Science of the Department of Computer Science and Information Engineering (DISI) of the University of Trento. There are no formal pre-requisites, but a certain familiarity with mathematical notation and with data bases is useful. This is a 14 week, 48 hours, six credit introductory course to Logic. The Logics covered are: propositional logics, first order logics, and Description logics. The focus is on modeling (i.e., how to use logics as a modeling language), semantics (i.e., the formalization of the intended semantics of the models developed), reasoning (with a focus on Tableau systems) and on the use of logics in practice (with a focus on the formalizational of natural language, ER models and relational data bases and knowledge graphs). The course has a strong emphasis on examples, exercises and practical applications of Logic, with a focus on Artificial Intelligence and Computer Science applications.
  • Knowledge Graph Engineering (KGE). This course is taught in English. The intended target are the students of the master degree in Computer Science of the Department of Computer Science and Information Engineering (DISI) of the University of Trento. There are no formal pre-requisites, but knowledge of the modeling languages (mainly ER Models), of the Web languages (mainly RDF and OWL), of some basic notions of Machine Learning and of Python will help a lot. This is a 14 week, 48 hours, six credit, advanced course on how to develop a Knowledge Graph (KG) starting from data – to be cleaned and adapted – which are already available. This is a hands-on course. After a few introductory classes, students are given a problem to solve and they will build a knowledge graph solving this problem. A limited set of teaching material is available, mainly in form of slides. The student will learn mainly by doing the actual work and by interacting with the teacher and tutors. The exam consists in: writing a project report, giving a demo and making a public presentation.
  • Studies on Human Behaviour (SHB). This course is taught in English. The intended students are students of the master degree in Data Science of the Department of Computer Science and Information Engineering (DISI) of the University of Trento. The aim of this course is to study the behaviour of people. The course is data intensive and hands on. It covers all the phases from experiment design, data collection, data preparation and data analysis. After a brief theoretical introduction, the course will consist of running real world experiments, on large amounts of data. The exam will consist of presenting the results of the experiment in a public presentation. This inter-disciplinary course bridges competences in sociology, ethics and computer science.
  • Towards Brain Programming. This course is for PhD students of the Department of Computer Science and Information Engineering (DISI) of the University of Trento. This course will introduce a new computational model of the brain, called SPA (for Sensing Processing Acting) currently under development, and a platform implementing it, called Faisca. The ultimate goal of Faisca is to allow for the development of synthetic brains, simulating the human brain in its main functionalities. Faisca is programmed in  BL (for Brain Language). The course will describe the SPA underlying principles, as well a first implementation of BL and Faisca. The model and the platform will be motivated by mapping them to various Cognitive and Neuroscience notions, in particular: the nervous system, the brain, neurons and glial cells. The course will be interactive alternating explanatory lectures with hands-on BL development.

    (*) Faisca is being developed as a joint effort of the University of Trento and the University of Minho. We expect to release a first version open source in the spring 2022.

  • AI in Everyday Life (School of innovation). Artificial intelligence (AI) is a key driver of the Fourth Industrial Revolution, transforming all sectors of society, from business and health to education. "Targeted" or "Restricted" AI (Narrow AI), applications aimed at carrying out very specific tasks, have been evolving more and more in recent decades. From the use of technologies such as "Siri" or "Alexa" which help us make a phone call, or the use of the Google search engine which returns us the information we requested in a few seconds or even when we discover photos on social networks where we use targeted applications AI. In this course, we utilize the participants’ familiarity with such applications, in order to explore the technical, but also social and ethical aspects, of the modern AI system.
    Narrow AI differs fundamentally from the General AI (e.g., humanlike agents and even humanoid robots) that are often portrayed in science fiction novels and movies, which tends to influence the public’s beliefs about what AI is and where AI research is headed in the near future. In fact, General AI is - according to most scientists - neither possible nor desirable. Thus, this course focuses on Narrow AI applications, which can augment human capabilities, and which are playing an increasing role in the workplace.
    The overall objective of the learning unit is to help participants appreciate what AI actually is, as well as to understand the basic elements of AI technologies, and how they are developed. Learners will familiarize themselves with the fundamental definitions and concepts around AI, but also the widespread methods used in the creation of systems. In particular, an introduction to data-driven AI, based on machine learning, is provided. The role of personal data in the AI ecosystem - provided to companies via the use of services such as search engines and social media - will be discussed.
    In addition to examining the fundamental concepts surrounding the technical aspects of Narrow AI, we will also examine the ethical and social implications of its widespread use. Participants will therefore learn to analyze the functions of "everyday" AI applications and will be able to understand the potential risks of these technologies, in an effort to raise awareness, encouraging them to critically evaluate them.
    In short, the ultimate goal of this course is to empower participants to make use of AI applications - particularly those that are playing an increasing role in everyday life - in an optimal way.
    The course will be taught in English. 2 credits. The course material is online, including the evaluation tests. The lecture hours will be used to answer the students questions and to collect feedback.