Artificial Intelligence for Management

The course aims to explore the principles of financial statement analysis. It provides the participants with a framework to understand how businesses’ value and risks are captured in financial statements and price them correctly.

The course is focused on understanding of the opportunities arisen from digital transformation, in terms of innovative business models for companies and new value for users. Specifically, the course will deepen technology forecasting and technology roadmapping methodologies, applied to emerging digital trends, focusing on Fintech, IoT and Artificial Intelligence.

Meoli Azzurra

Today it is crucial to be able to use data for making better marketing decisions. This is possible by better understanding, predicting and managing customers’ behavior in a landscape where customers and brands (firms) are decision makers. This course focuses on the applied use of various techniques/methods, theories and approaches from marketing literature in practical business cases. In more particular words this means exploring, investigating and predicting behavioral and attitudinal (customer) data to provide data-driven answers to relevant marketing questions

The aim is to provide basic knowledge concerning the main concepts and principles of computer security (e.g., risk, tools for risk assessment and evaluation, attacks and their typical structure, resources, functional systems requirements, human component). In this way, the course will provide the basic tools for the design and implementation of reasonably secure systems. During this process, either methodological, technological and behavioral aspects (i.e. operation security) need to be considered.

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.

The digitization of the economy is one of the most relevant issues of our time. The objective of this course is to analyze how digital economy has fundamentally challenged traditional business models and created new business opportunities. We will start with a brief introduction of the digital economy and then analyze the specific business strategies adopted by the different players in this ecosystem, specifically platforms. We will also discuss the implications for public policy and regulation. In particular, we will consider whether new business practices and contracts in the digital economy are beneficial or detrimental to society. Real case studies related to Amazon, Airbnb, Booking.com, Facebook, Google, Uber, and others, will be analyzed.

The course focuses on the finance aspects following the life cycle of young and innovative ventures from their start-up. These young ventures usually require substantial outside financing in early stages to eventually create employment, growth, social contributions and tax revenues in their future. Bank financing is hardly available for these ventures and therefore, all funds need to be raised from other sources, e.g. friends and family, business angels, or from professional financial intermediaries, so-called venture capital and private equity funds. Besides those traditional channels, ventures can raise funds from “the crowd” using digital platforms (crowdfunding) or blockchain-based technologies (ICOs).

The activity is aimed at proposing innovative business processes focused on digital technologies and new business models.

The course provides the application of the skills students improved during the classes by developing a practical project based on a real company case.

Companies involvement will be one of the pillars of this course. Companies will assign the project guidelines to the students, building them around the class profile, and will supervise the development of their projects, giving help if needed.

This course provides an introduction to the basic principles and methods of Data Mining and Machine Learning, with emphasis on Classification, Clustering, Association Rules, Outlier discovery. Analysis of the main problems related to data quality and data transformation. Python will be used as part of the course to solve machine learning problems.

Neural networks are a class of machine learning algorithms, originally inspired by the brain, structured in layers of interconnected artificial neurons.

The network can be trained on data to optimize its connections to a specific task.

Deep neural networks, that is networks with multiple internal (so called hidden) layers, have recently seen a lot of success at practical applications. They’re at the heart of production systems at companies like Google and Facebook for image classification, speech recognition, natural language processing, language understanding or robotics.

The course gives an overview of the foundational ideas and the recent advances in neural nets, explaining the potentialities of the topic for practical purposes. We shall cover supervised and unsupervised techniques, methods for visualizing and understanding the behavior on neural nets, as well as adversarial techniques to fool them. We shall also hints to recent applications in the field of reinforcement learning, and some amazing results in game simulation.

The course get the focus on the relevance that Artificial Intelligence software have in the development of strategic companies activities like customer care, processes automation and information management. Lessons give an overview on how Industry 4.0 companies may use Artificial Intelligence for their businesses, covering a wide range of tasks in Natural Language Processing such as sentiment analysis, topic classification and dialogue systems.

Computer vision pursues understanding the content of digital images through a large corpus of techniques drawing from a variety of diverse fields. These include, among the others, signal and information theory, calculus, geometry and linear algebra, probability and statistics, optimization, machine learning.

Computer vision has emerged through the last three decades as a key process technology to realize inspection and assembly of manufactured goods. Moreover, thanks to recent breakthroughs in image sensing and analysis, we now witness ever-increasing deployment of computer vision within products as widespread as  cars, gaming consoles, apps, personal computers, home appliances.

The course will present an overview of some of the most effective computer vision techniques alongside with examples dealing with both “in process” and “in product” applications.

The course deals with the digital revolution in telecom services. It presents the telecom service paradigms and discusses the opportunity and chalenges of integration introduced by the digital transformation of the communication. Then the most recent technologies supporting the modern telecom service paradigm are discussed, from network softwarization to IoT network architectures and service deployments.

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

The course addresses the main ethical, political, and legal issues related to AI, with particular attention to the myth of artificial intelligence ethics, the political properties of information systems, and Italian and European regulations on AI. The conceptual analysis will be accompanied by the discussion of concrete cases. Participants will thus develop the ability to recognize fallacies, myths, and misleading narratives — which lead to the hasty adoption of immature products — and to identify realistic automation pathways that comply with fundamental rights.

This course explores how AI is transforming the natural evolution of businesses, reshaping processes, decision-making, and entire business models. We examine what it means to build an AI-first organization and the emerging roles that guide this shift, such as the AI Strategist and AI Solution Architect. Through concrete cases and practical tools, we show how AI supports continuous innovation and strategic experimentation. The focus is on using AI not just to automate, but to rethink how companies create value and adapt over time. Students learn how to steer business transformation through evidence-driven, AI-enabled evolution.

The course examines Change Management as a key enabler for the effective adoption of Artificial Intelligence in organizations. It focuses on the organizational, cultural, and managerial impacts of AI, the management of resistance to change, and the role of leadership in driving data-driven transformations.

This course is designed to equip students with practical skills in No-Code development. The program covers the fundamentals, including benefits, limitations, key tools, and the intersection with AI. Hands-on modules include the development of websites, landing pages, AI-powered chatbots, AI agents, and automation systems, all built using No-Code tools and Vibe Coding.

This course offers a practical introduction to Large Language Models (LLMs) and multimodal AI systems, guiding participants from the fundamentals of neural networks to modern generative and agentic capabilities. Through conceptual explanations, real-world examples, and hands-on demonstrations, the course aims to show how LLMs process information, how to interact with them effectively through prompt engineering, and how they can be integrated with tools, automations, and external knowledge sources. The goal is to equip attendees with the skills to understand, use, and apply LLMs confidently within professional and organizational contexts.