Instructional goals
The primary instructional goals of the AI Literacy course are to empower students with a foundational understanding of Artificial Intelligence (AI) and its diverse applications, enabling them to navigate an increasingly digital and technological landscape.
Prerequisites
Students are expected to have a general understanding of digital tools and basic computer skills. While prior knowledge of Artificial Intelligence is not mandatory, a general curiosity and interest in technology and its societal impact will be beneficial.
Intended learning outcomes
By the end of this course, students will be able to: - Grasp the fundamental concepts, principles, and evolution of Artificial Intelligence. - Recognize the applications of AI in everyday life and professional environments. - Critically evaluate the benefits, challenges, and risks associated with AI technologies. - Engage in informed discussions on the ethical and social implications of AI.
Course Contents
The course is divided into two parts: a theoretical introduction and practical hands-on sessions. It begins with foundational AI concepts, including its subfields like machine learning and natural language processing, and continues with practical applications such as prompt engineering, automation with AI in productivity tools, multimedia generation, and multimodal data analysis. Students will also explore the societal and ethical implications of AI.
Reference Books
There is no single textbook for this course. Key materials and references will be provided during the lectures and hands-on sessions.
Teaching Methods
The course combines traditional lectures with practical hands-on sessions. Classes are entirely in-person, fostering direct interaction and collaborative learning. Theoretical concepts will be complemented by interactive activities, discussions, and AI tool-based exercises.
Assessment Method
Assessment will be based on class attendance and participation.
Thesis assignment criteria
The final project or thesis will be assigned to students based on their demonstrated interest and engagement with the subject matter.
Week 1
The course introduces AI and its subfields, such as machine learning and deep learning. A special emphasis will be given to the sub-field of deep learning represented by generative AI. The evolution and history of AI will be explored, with a focus on major milestones and breakthroughs.
Week 2
Students will be introduced to the fundamental algorithms of classical machine learning, both supervised and unsupervised learning, as well as clustering and reinforcement learning techniques. Practical examples and tools will be used to assist in understanding the topics.
Week 3
Starting from natural learning processing techniques, students will follow the path leading to the evolution of large language models (LLMs), which underpin generative AI. The main current tools of generative AI will be examined in depth, showing their strengths and weaknesses. The basics of prompt engineering will be provided to exploit the full potential of LLMs
Week 4
Discussion will center on ethical challenges and societal implications of AI, including issues like bias, privacy, governance, and regulatory frameworks. The potential future developments of AI and its role in shaping society will also be considered. In particular, the impact of AI on equity, the environment and economic development will be analysed.
Week 5
Hands-on activities will introduce generative AI, focusing on question answering and text classification. Students will design and test prompts and strategies to create effective question-answering models and implement text classification. They will also address security issues and how LLMs can be the scene of cyber attacks
Week 6
Students will leverage the prompt engineering skills acquired during theoretical lectures to exploit the use of generative AI tools as copilot for the generation of code to be used for the creation of apps and games.
Week 7
This lesson focuses on generating code, using python as a base, for analysing data and creating graphs to extract value from data that would otherwise not emerge. Students will understand how they can use the basics of python combined with the help of an LLMs to be able to create fast and efficient processing of financial datasets. A previous knowledge of python is helpful, but not necessary.
Week 8
The content of the previous lesson is further explored, and students are challenged to exploit generative AI tools to create a presentation of the work done in the previous lab sections.
Week 9
This lesson involves a summary of previous lessons, and a final challenge is set for students to solve using the tools shown and used in the course, encouraging their creativity and stimulating a conscious use of generative AI