By the end of the course, students will be able to understand, analyze, and critically discuss the principles of Artificial Intelligence applied to Marketing and Communication. They will develop the skills required to identify relevant business problems and to design and implement effective AI-based solutions. Students will become familiar with the most important AI techniques, tools, and methodologies relevant to Marketing and Communication. In addition, the course provides a comprehensive understanding of the entire lifecycle of data-driven applications, enabling students to support and contribute to data-driven transformation initiatives within organizations.
Prerequisiti
Students are expected to have a basic understanding of linear algebra, probability, and statistics, as well as the ability to program in Python.
Metodi didattici
Approximately 60% of the course will be delivered through lectures, during which the fundamental principles and techniques of Artificial Intelligence will be introduced and illustrated through real-world examples and case studies. The remaining 40% of the course will consist of hands-on laboratory sessions, where students will learn how to address and solve Artificial Intelligence problems using the Python programming language and related tools.
Verifica Apprendimento
The assessment consists of two components: • A written examination, accounting for 50% of the final grade, which includes multiple-choice questions, true-or-false questions, and open-ended questions designed to evaluate students' understanding of the course concepts and methodologies. • A final project presentation, accounting for the remaining 50% of the final grade, which will be developed during the laboratory sessions and presented at the end of the course. The final grade will be determined by combining the results of these two assessment components.
Testi
The course is primarily based on the lecture slides and materials provided by the professor throughout the semester. Recommended books: Suriano, S., Di Domenica, N., Fusi, M., & Capone, L. (2023). Advanced Analytics and Artificial Intelligence for Marketing: Cases and Applications. Pearson, Milan. Suriano, S., Bertino, E., & Di Domenica, N. (2025). Generative AI and Marketing: Opportunities, Challenges, and Business Applications. McGraw Hill Education, Milan.
Contenuti
Introduction to Marketing with a Data-Driven Approach: This section introduces the fundamental concepts and methodologies required to conduct quantitative marketing analysis, emphasizing the role of data in supporting marketing decisions. Machine Learning and Deep Learning Models for Customers, Products, and Engagement: This section provides a methodological overview of the design, development, and interpretation of advanced analytics and artificial intelligence models. Particular attention is given to customer analytics, product analysis, and the optimization of marketing strategies and engagement channels. Evaluation and Monitoring of Marketing Activities: This section examines the principles and techniques used to assess marketing performance. Students will explore key performance indicators (KPIs) and metrics for measuring campaign effectiveness, incremental revenue generation, and return on investment (ROI).
