There are no specific prerequisites for this course. However, it is recommended that students have a basic understanding of programming and computer science, which can be acquired by taking the “Computer Science for Cultural Heritage” course in the first semester or another introductory computer science course. During the course, certain concepts from basic calculus will be covered in a simplified manner (e.g., sets, functions, and the minimum and maximum values of a function)

- Definition of Artificial Intelligence. Sub-areas of Artificial Intelligence. The link between Intelligence and learning. Main human and mammals learning mechanism.

Current most used conversational AI interfaces and underlying models for text creation.

-Current most used conversational AI interfaces and underlying models for image creation. Prompt engineering. Creation of a structured prompt using personas.

-Recall of mathematical concepts useful to understand the basics of Machine learning: Concept of function, set, domain and range/co-domain. Math functions in R2 , Graph of a function, Function notation, maximum and minimum of a function. General equation of a line in R2.

-Introduction to Machine learning. Definition of Machine Learning, the relationship between statistics and machine learning in relation to their different objectives and the use of the same models. Causality and correlation in statistics. Dependent and independent variables. The regression models. Linear regression with two variables. Supervised learning and the linear regression as a supervised ML model. Labeled data. Using the Gradient Descent algorithm to find the parameters of the linear regression model. Using conversational AI to plot a dataset, finding parameters for linear regression given a dataset.

- Machine learning. Supervised learning: classification. Types of classification. Supervised classification algorithms/models: Logistic regression, K-nearest-neighbors, classification/decision trees (CART).

 Using conversational AI to plot a dataset, finding parameters for logistic regression given a dataset.

- Machine learning. Unsupervised learning: k-means clustering. Exercise on google colab.

Reinforcement learning. Machine learning steps. How to choose a model.

-Neural networks and deep learning. The components of a neural network. Deep learning for image classification. How a computer stores pictures: tensors. Convolutional Neural networks.

-AI use cases for cultural heritage.

-AI social and ethical challenges. Hallucinations. Algorithmic bias and discrimination. Copyright issues. Deep fakes, misinformation. Energetic issues. The European AI ACT.

The course aims to introduce students to the key concepts of artificial intelligence, enabling them to independently keep pace with its ongoing developments. As they work on a year-long project, the students learn how to achieve design objectives by integrating conversational AI tools into the design process.

Lectures and practical assignments. 

oral exam plus project grade

Teaching material (slides, datasets, useful links) is made available on the teams channel of the course. 

Some topics are covered by the book:
Machine Learning For Absolute Beginners: A Plain English Introduction (Learn AI & Python for Beginners). Oliver Theobald. (2018)