Instructional goals
Activities will be conducted entirely in-person, and remote participation is not possible. This course offers all graduate students the opportunity to build a solid foundation in Artificial Intelligence—one of the most in-demand skills in today’s job market. Through a combination of theoretical instruction and practical application, students will explore core AI concepts, its most relevant real-world applications, and the ethical and societal implications surrounding its use. The course is designed to equip students with the critical tools needed to understand and navigate the challenges of an increasingly technological and fast-evolving world. It represents a unique opportunity to enrich your academic profile and prepare for a job market that is rapidly shifting toward digital innovation. Upon successful completion of the course, you will receive: . 4 ECTS credited to your study plan . A badge that can be shared on LinkedIn and other professional platforms
Prerequisites
No Prerequisites.
Intended learning outcomes
By the end of the course students will be able to: 1. Understand the fundamental concepts, principles and evolution of AI. 2. Recognize the applications of AI in everyday life and professional contexts. 3. Critically evaluate the benefits and risks associated with AI technologies. 4. Participate in informed discussions about the social and ethical implications of AI.
Course Contents
The AI Literacy course offers graduate students the opportunity to gain a solid background in Artificial Intelligence (AI), one of the most demanded skills in today's job landscape. Through a theoretical and practical approach, the course will explore the fundamental concepts, the most relevant applications, and the ethical and social implications of AI, providing students with the tools needed to understand and meet the challenges of an increasingly technological and evolving world. A unique opportunity to enrich the academic profile and prepare for a job market increasingly focused on digital innovation. The first part, that will last 4 weeks, includes theoretical classes, during which the fundamental concepts of Artificial Intelligence will be introduced. This first part will be followed by a second part of 5-week laboratory sessions, during which students will apply what they have learned. These activities have been designed in line with the specific educational objectives of the respective Degree Courses. In the following the course content has been described in more details. Part I The framework of this course aims to provide students with a foundational understanding of Artificial Intelligence (AI) and its applications across various domains. By the end, successful students will be able to: · Understand the basic concepts, principles, and evolution of AI. · Recognize AI in everyday life and professional contexts. · Assess the potential benefits and risks of AI technologies. . Engage critically and participate in informed discussions about AI and its societal implications. Part II Equip students with practical skills to leverage generative AI models for various tasks, including use cases in the domain of economics and management, problem-solving, and automation, with the ultimate goal of enhancing work efficiency. The sessions focus on conversational AI, prompt engineering, evaluation of model outputs, multimodal capabilities, and task automation. By the end of the course, participants will be able to: · Understand and compare generative AI models · Use prompt engineering to solve complex problems · Evaluate the reliability and quality of AI-generated outputs · Leverage multimodal AI for diverse business applications . Implement basic automation using AI tools
Reference Books
Books: • Grokking Machine Learning, Luis G. Serrano • “Artificial Intelligence: A Guide for Thinking Humans” by Melanie Mitchell • “Artificial Intelligence Basics: A Non-Technical Introduction” by Tom Taulli
Teaching Methods
For a 9-week AI Literacy course comprising 4 weeks of theory and 5 weeks of labs, various tech-enhanced teaching methods can support active learning and skill development. In the theoretical phase, a flipped classroom approach allows students to review materials before class, enabling deeper in-class discussions and critical debates. Lectures can be complemented by interactive demonstrations of AI systems and the analysis of real-world case studies to explore both technical foundations and societal implications. Role-playing and simulations can help students engage with ethical dilemmas related to AI, fostering reflective and informed perspectives. During the lab sessions, students can participate in prompt engineering exercises, iterative design challenges, and mini hackathons to practice applying AI concepts. They may also conduct critical evaluations of existing AI systems, focusing on issues such as fairness, transparency, and accountability. Design thinking methodologies can guide students in developing practical AI-based solutions to real-world problems. The course will conclude with a Prompt-a-thon, where students collaboratively address a real case study, putting their learning into action in a high-impact, team-based environment.
Assessment Method
Participation in the course and challenge is mandatory and grants the 4 ECTS in your study plan for GAP1 and GAP2 activities. The course will end with a practical event, the “Prompt-a-thon,” lasting 8 hours. Students will work on a real-world case study, chosen in collaboration with the Director of the Course of Study and a corporate partner. Students will be eligible and thus acquire course credits if their participation in at least 80 percent of the lectures and the final Prompt-a-thon is confirmed. Activities will be conducted entirely in-person, and remote participation is not possible.
Thesis assignment criteria
No thesis.
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. Links • Some of the slides of this class are based on the free course Introduction to Deep Learning Fidle is a free, open-access online training program offered by CNRS, Université Grenoble Alpes, and MIAI. • Andrew Moore’s tutorials on machine learning: http://www.cs.cmu.edu/~awm/tutorials.html • Google Courses on machine learning https://developers.google.com/machine-learning Materials: Slides, lecture notes and course material will be made available (preferably before each class) on the official e-learning platform.
Week 2
AI Techniques and Algorithms: Understand key AI techniques and algorithms. Links • Some of the slides of this class are based on the free course Introduction to Deep Learning Fidle is a free, open-access online training program offered by CNRS, Université Grenoble Alpes, and MIAI. • Andrew Moore’s tutorials on machine learning: http://www.cs.cmu.edu/~awm/tutorials.html • Google Courses on machine learning https://developers.google.com/machine-learning Materials: Slides, lecture notes and course material will be made available (preferably before each class) on the official e-learning platform.
Week 3
AI Applications: Examine AI applications in various fields. Links • Some of the slides of this class are based on the free course Introduction to Deep Learning Fidle is a free, open-access online training program offered by CNRS, Université Grenoble Alpes, and MIAI. • Andrew Moore’s tutorials on machine learning: http://www.cs.cmu.edu/~awm/tutorials.html • Google Courses on machine learning https://developers.google.com/machine-learning Materials: Slides, lecture notes and course material will be made available (preferably before each class) on the official e-learning platform.
Week 4
Ethical and Societal Implications: Discuss the ethical challenges and explore the societal impact of AI (including regulations and governance). AI and the Future: Consider potential future developments in AI and its potential role in the future of society. Links • Some of the slides of this class are based on the free course Introduction to Deep Learning Fidle is a free, open-access online training program offered by CNRS, Université Grenoble Alpes, and MIAI. • Andrew Moore’s tutorials on machine learning: http://www.cs.cmu.edu/~awm/tutorials.html • Google Courses on machine learning https://developers.google.com/machine-learning Materials: Slides, lecture notes and course material will be made available (preferably before each class) on the official e-learning platform.
Week 5
Conversational Models: Use Cases and Comparisons o Overview of conversational AI models (e.g., GPT-4o, Claude, Gemini) o Comparing LLM of different families and sizes (e.g., GPT-4o vs GPT-4o mini) o Impact of fine-tuning and customizations (e.g., ChatGPT vs Bing, Copilot) o Hands-on exploration of capabilities and limitations Materials: Slides, lecture notes and course material will be made available (preferably before each class) on the official e-learning platform.
Week 6
Prompt Engineering for Conversational Models o Techniques: zero-shot, few-shot, chain-of-thought reasoning, and persona-based prompting o Practical application: crafting structured responses, generating actionable insights, and optimizing outputs for a use case related to economics and management Materials: Slides, lecture notes and course material will be made available (preferably before each class) on the official e-learning platform.
Week 7
Evaluating Model Outputs and Handling Errors o Techniques for assessing response quality o Detecting and understanding hallucinations o Assisted research (e.g., using ChatGPT for search) o Exploring models that perform actions o Exploring how the model reacts to prompt forcing, and response drift to empathize with the user Materials: Slides, lecture notes and course material will be made available (preferably before each class) on the official e-learning platform
Week 8
Multimodal Models for Complex Business Use Cases o Introduction to multimodal AI (e.g., Gemini, DALL-E) o Hands-on activity: integrating text and visual inputs to address a complex task o Business application: generating presentations, visual content, and analytics from multimodal inputs Materials: Slides, lecture notes and course material will be made available (preferably before each class) on the official e-learning platform.
Week 9
AI-Assisted Automation: o Automating workflows: creating Excel formulas, macros or small scripts for data processing and analysis Materials: Slides, lecture notes and course material will be made available (preferably before each class) on the official e-learning platform.
Week 10
There is no week 10.
Week 11
There is no week 11.
Week 12
There is no week 12.