The module is intended as a technical introduction to the topic of conversational artificial intelligence, following a path of practical activities, comparisons, and reflections useful for a deeper understanding of the current technological revolution. The first phase of the course focuses on personal perceptions of artificial intelligence, the doubts and certainties everyone has about the massive introduction of intelligent technologies into everyday life. Then, through an in-depth historical and cinematic study, the generative opportunities of current artificial intelligence will be presented through freely accessible online tools such as Suno, Gamma, NotebookLM, and Gemini. A crucial point of the course is the need to understand how to best interact with generative AI tools, and in addition to practicing the fundamentals of prompt engineering, you will be provided with the knowledge necessary to evaluate the best tool to achieve your goals, both in business and personal

This module introduces the key concepts and tools of artificial intelligence and data
science, taking a practical, visual, and business-oriented approach. Divided into three
sessions of 4 hours each, the module includes:

• Friday afternoon – Fundamentals, Data, and Machine Learning: An overview of artificial intelligence, data science, and big data; data representation in space through embedding; data visualization; and key supervised and unsupervised machine learning techniques, with examples of clustering, classification, and prediction based on real-world business cases
• Saturday morning – Deep Learning and NLP: Introduction to neural networks and deep learning, convolutional neural networks (CNNs), object detection and segmentation, fundamentals of natural language processing, transformer architectures, and an overview of generative AI and large language models, with practical examples involving images, text, and documents
• Saturday afternoon – Hands-on workshop with Orange Data Mining: a guided activity in a visual environment to explore data, build analysis workflows, apply visualization techniques, train machine learning models, and interpret the results without the need for programming

Each session includes a brief theoretical introduction, an overview of the methodology, and a practical
component featuring concrete examples and tools that can also be used in non-technical contexts.

The module will address the issue of data-awareness as a necessary condition for implementing AI use strategies. Beyond the rigid issues of compliance, the goal is to clarify the strategic value of data ethics and data strategy: to understand implications and risks of data use, to recognize the need for contextualization as an act of governance, and to develop awareness of the role data plays in the construction of knowledge, relationships, and power. A course designed for decision makers in high complexity environments, especially in contexts enhanced by the use of AI.

This module provides a comprehensive understanding of the generative artificial intelligence ecosystem, tracing the evolutionary path from the earliest forms of machine learning to today's large language models (LLMs). The inherent capabilities and limitations of LLMs will be explored, understanding how technology gaps are filled through innovative approaches and advanced architectures. Through the analysis of concrete applications such as ChatGPT, the module will unveil the operational mechanisms that make these technologies possible, unabashedly addressing critical challenges such as algorithmic biases and hallucinations, and presenting practical strategies for managing them.

The training delves into specialized techniques such as Retrieval-Augmented Generation (RAG) and fine-tuning, providing the skills needed to evaluate the enterprise implementation of AI solutions. Alternative models such as small language models and open source solutions will be explored, along with emerging multi-agent architectures that are redefining the technology landscape. 

A significant section will be devoted to the human dimension of AI, analyzing the evolution of the person-machine relationship and presenting real-world case studies of adoption, complete with analysis of critical issues and success factors. The goal is to provide all students with a solid theoretical and practical foundation to navigate the integration of generative AI into real-world professional contexts with awareness.

The module is a hands-on, experiential immersion that will guide you to build an AI Agent that can do what you most desire and will turn your work life around. The "AI Agent" workshop will begin with a brainstorming phase, continue with building the agent in which you will be mentored by the lecturer and tutor, and then have the opportunity to present your work in front of the class.

The module provides a more technical overview of what lies behind the operating principles of AI. Its educational objective is to enable participants to understand and apply advanced computational reasoning techniques, such as Chain-of-Thought prompting, iterative self-reflection, and the use of AI Judge for response optimization. The focus will be on theory, followed by numerous practical cases and exercises on APIs keys, no code, and low code, using Google Colab. 

In fact, the approach is practical, expendable, and concrete, with many references to daily work life. In fact, the program includes real-world benchmarks (GSM8K, MMLU, BIG-bench) to evaluate model performance and incorporates best practices for writing effective prompts, ensuring experience that is immediately applicable in daily work. In summary, the module provides concrete tools to harness the potential of LLMs in reasoning, enhancing participants' analytical and decision-making skills.

The final module is a hands-on workshop designed to put the knowledge gained throughout the course into practice. Based on the class’s needs, students are given briefs on real-world business cases to help them manage processes, people, and technologies as they transition to artificial intelligence.