Instructional goals
This course provides students with a foundational understanding of Artificial Intelligence (AI) and its applications across various domains. By the end of Part I of the course, students will be able to: • Grasp AI fundamentals, understanding the core concepts, principles, and historical evolution of AI • Identify AI technologies in everyday life and professional contexts • Assess the potential benefits, challenges, and risks associated with AI technologies • Engage critically and participate in informed discussions about AI and its societal implications Part II of the course will ensure students are equipped to integrate AI technologies into their marketing strategies effectively and responsibly. By the end of this part, students will be able to: • Leverage Generative AI tools for content creation across text, image, audio, and video domains • Use low-code tools such as Langflow for the development of customized GenAI applications. • Identify AI technologies in marketing and related fields • Develop AI-enhanced marketing strategies
Intended learning outcomes
Students understand the foundational concepts of Artificial Intelligence and know how to use tools and basic development techniques.
Course Contents
AI fundamentals: history, core principles, key techniques and algorithms (ML, NLP, Generative AI) Implications and context: applications across industries, ethical, social, and regulatory aspects Tools and techniques: prompt engineering, data visualization and analytics, low-code tools (e.g., Langflow) Marketing applications: AI for campaigns, branding, content creation, and entrepreneurship
Reference Books
- Andrea De Mauro: Data Analytics Made Easy: Analyze and present data to make informed decisions without writing any code. Packt, Birmingham, 2021. ISBN: 9781801074155. - Andrea De Mauro: AI Applications Made Easy: RAG, agents, and other GenAI solutions. Manning, 2025. ISBN: 9781633435872.
Teaching Methods
Hands on sessions and in class discussion
Assessment Method
To pass this course, the student must reach the threshold for presence in class and complete the final group work. Evaluation will be: Pass/Not pass.
Thesis assignment criteria
Week 1
Introduction to AI • Describe AI and its subfields (e.g., machine learning, deep learning, natural language processing, Generative AI) • Explore the history and evolution of AI
Week 2
AI techniques and algorithms • Understand key AI techniques and algorithms
Week 3
AI applications • Examine AI applications in various fields • AI applications in marketing (e.g., customer segmentation, sentiment analysis for brand monitoring, predictive analytics for campaign success)
Week 4
Ethical and societal implications of AI and its role in shaping the future • Discuss the ethical challenges and explore the societal impact of AI (including regulations and governance) • Potential future developments in AI and its potential role in the future of society.
Week 5
Introduction to LLMs, FMs, and GANs • Generative AI: basic concepts, model training, fine-tuning • Applications (e.g., text generation, summarization, translation, conversational agents, computer vision, multimodal applications)
Week 6
Prompt engineering techniques • Zero shot, few shots, chain-of-thought reasoning, persona patterns • Practical examples
Week 7
AI for data analytics and visualization • Generative AI for visualization and predictive analytics • Hands on session
Week 8
Building apps with LLM • Introduction to Langflow • Hands-on session
Week 9
AI for marketing, branding, advertising, and entrepreneurship • Leveraging AI to optimize marketing strategies and advertising campaigns • Developing branding with generative AI tools