Associate Professor of Computer Science University of Bologna

Roberto Amadini is associate professor in Computer Science at the University of Bologna since 2022. He received his Ph.D in Computer Science from the University of Bologna in 2015. He was research fellow at the Department of Computer Science and Engineering, University of Bologna, from 2015 to 2016, Research Fellow at Department of Computing and Information Systems, University of Melbourne, Australia, from 2016 to 2019, and Senior Researcher at the Department of Computer Science and Engineering, University of Bologna, from 2019 to 2022.


Artificial intelligence (AI) is a discipline whose goal is to realize this dream through the use of a wide variety of techniques, from logic-based symbolic computation to sub-symbolic models inspired by the structure of the brain, such as neural networks. This course will provide an introductory overview of the various existing artificial intelligence techniques, focusing on industrial applications, discussing future challenges and opportunities, and also addressing some of the social, economic, and ethical implications.

Data Science and Business Analytics
Artificial Intelligence and Innovation Management
Finance and Fintech
Data Marketing and Analytics

The course will give some insights of Decision Support Systems from two orthogonal perspectives: an optimization-based approach, based on symbolic AI (1st part) and a data-driven approach, based on sub-symbolic AI (2nd part).
Upon completing the entire learning process, students should be able to:
• get an overall idea of what DSS are, and how to handle them from different perspectives
• identify decision problems addressable with a data-driven approach
• know the basics of modeling with constraints and MiniZinc

Artificial Intelligence and Innovation Management

The landscape of business decision-making has undergone a profound transformation, courtesy of artificial intelligence. This evolution has the potential to bring forth pivotal advantages—enhanced accuracy, heightened efficiency, and a remarkable ability to unravel intricate patterns. Integration of AI into the decision-making processes not only holds the potential to refine precision but also positions organizations to secure a competitive edge through well-informed, data-driven, and strategically sound decisions.

In the first part of the course, we address the critical trade-offs involved in adopting Machine Learning methods for decision-making in business contexts. The course equips participants with both conceptual and practical skills needed to assess the costs—both social and economic—against the tangible benefits that these systems bring to the realm of corporate innovation.

In the second part of the course, we address how Artificial Intelligence can be used for (combinatorial) optimisation, in alternative to, or in combination with, Machine Learning. We introduce the main features of the Constraint Programming paradigm and the MiniZinc language, to show how this approach can be used to build Intelligent Decision Support Systems.

This is not an abstract exercise. Through in-depth case studies, students dissect the real-world complexities associated with AI adoption, gaining insights into the nuanced landscapes of risks and opportunities. The curriculum demands an interdisciplinary mindset, where technical expertise aligns seamlessly with the practicalities of business strategy, fostering a robust understanding of how AI amplifies decision-making capabilities.

Artificial Intelligence for Business