Obiettivi formativi
This course aims to develop students’ foundational understanding and critical awareness of Artificial Intelligence (AI) technologies, as well as their practical ability to apply generative AI tools in managerial contexts. Through a blend of theoretical insights and hands-on sessions, the course will: Demystify key concepts, techniques, and historical developments in AI. Enable students to recognize and evaluate the role of AI in business and society. Provide practical experience with leading AI models to enhance analytical thinking, creativity, and decision-making. Encourage ethical reflection and responsible use of AI technologies in professional settings.
Prerequisiti
There are no formal prerequisites for this course. However, basic digital literacy and an interest in emerging technologies, particularly Artificial Intelligence, will be beneficial.
Risultati di apprendimento attesi
Upon successful completion of this course, students will be able to:
Explain foundational concepts and the historical evolution of AI.
Identify key AI techniques and subfields, including machine learning and generative AI.
Analyze real-world applications of AI in various domains, particularly in economics and management.
Critically assess the ethical, societal, and regulatory implications of AI technologies.
Employ generative AI tools effectively for business problem-solving, content creation, data analysis, and automation.
Evaluate and improve the quality of AI-generated outputs through prompt engineering and critical review.
Integrate multimodal AI tools into practical business scenarios.
Contenuti Del Corso
The course is divided into two main parts:
Part I – Common Framework (Theory-Oriented)
Introduction to AI
AI Techniques and Algorithms
AI Applications
Ethical and Societal Implications
AI and the Future
Part II – Program-Specific (Practice-Oriented for Management)
Conversational AI Models and Use Cases
Prompt Engineering for Business Applications
Evaluation of Model Outputs and Mitigation of Errors
Multimodal AI in Business Contexts
AI-Driven Task Automation and Workflow Optimization
Testi Di Riferimento
Slides and other learning materials will be provided by the instructors. No mandatory textbooks are required.
Metodologie Didattiche
Interactive lectures and class discussions. Hands-on lab sessions and demonstrations. Prompt engineering workshops. Individual and group projects. Final "Prompt-a-thon" challenge
Modalità di verifica dell'apprendimento
Active participation and attendance. Completion of group projects assigned during class. Participation in the final Prompt-a-thon challenge
Criteri per l’assegnazione dell’elaborato finale
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Settimana 1
Introduction to AI. Classic Algorithms, Machine Learning, Data Science and AI comparison. AI history and evolution. Overview of AI techniques and applications. Machine Learning workflow and personas.
Settimana 2
AI techniques and algorithms. Classical machine learning: supervised, unsupervised, self-supervised, reinforcement learning.
Deep learning and applications. AI-driven organizations.
Introduction to embeddings and Large Language Models.
Settimana 3
Generative AI: Large Language Models. The Transformer architecture. Prompt Engineering techniques.
Multimodal AI: bridging text, image and video embeddings. Adversarial networks. Convolutional Neural Networks. Diffusion Models.
Settimana 4
AI Ethical and Societal Implications: Bias, fairness, surveillance, accountability, regulation. AI and the Future: General AI, AI-human collaboration, job transformation.
Settimana 5
Overview of conversational AI models. Comparing LLM of different families and sizes. Impact of fine-tuning and customizations. Hands-on exploration of capabilities and limitations.
Settimana 6
Prompt Engineering for Conversational Models. Techniques: zero-shot, few-shot, chain-of-thought reasoning, and persona-based prompting. Practical application: crafting structured responses, generating actionable insights, and optimizing outputs for a use case related to economics and management
Settimana 7
Evaluating Model Outputs and Handling Errors: Techniques for assessing response quality. Detecting and understanding hallucinations. Assisted research. Exploring models that perform actions. Exploring how the model reacts to prompt forcing, and response drift to empathize with the user
Settimana 8
Multimodal Models for Complex Business Use Cases: Introduction to multimodal AI. Hands-on activity: integrating text and visual inputs to address a complex task. Business application: generating presentations, visual content, and analytics from multimodal inputs
Settimana 9
AI-Assisted Automation. Automating workflows: creating Excel formulas, macros or simple Python scripts for data processing and analysis
Settimana 10
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Settimana 11
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Settimana 12
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