Basics of Artificial Intelligence

Overview courses

• Basic Principles of AI

  This course provides students with a foundational introduction to the interdisciplinary field of Artificial Intelligence. By the end of the course, students will understand what AI is, how it has evolved historically, and how it brings together ideas from computer science, mathematics, cognitive science, philosophy, and related disciplines. They will be able to place AI developments in a broader context and critically reflect on the strengths and limitations of different approaches.

• Basics of Machine Learning

  This course provides a fundamental introduction to machine learning, with an emphasis on conceptual understanding and practical interpretation. Students are introduced to widely used families of machine learning models, such as linear models, decision trees, neural networks, and unsupervised learning techniques. For each model type, the course discusses typical use cases, strengths, limitations, and considerations for interpretability. Attention is given to understanding how model outputs should be read, compared, and critically assessed in real-world settings.

• Frontiers of AI

  As AI is a rapidly developing research field, new techniques emerge at a fast pace and may have substantial impact when applied across different domains. This course provides an overview of recent and ongoing developments in Artificial Intelligence and their applications. It is organised as a lecture series including guest lectures from experts across different academic fields and societal domains.

• Responsible AI

  This course focuses on how AI systems interact with society, culture, and human values, rather than on how to build AI models. Students will learn to critically analyze technologies, understand their societal impact, recognize ethical risks such as bias, discrimination and opacity, and explore frameworks for designing, using and governing AI responsibly. 

• Applied AI

  The aim of the Applied AI course is to demonstrate and practise how AI can be used to address real-world problems in different fields, building on the concepts introduced earlier in the minor. The course revisits key ideas from previous courses and shows how they translate into practical applications, through guest lectures on state-of-the-art AI use cases and group assignments in which students apply AI concepts within their own domain of interest.