Fundamentals of Artificial Intelligence Programme (2024-2025)

Fundamentals of AI Programme (FAIP) is an elective course that is designed to teach master students the basics of Artificial Intelligence (AI). The aim of the course is to describe fundamental concepts and techniques of AI, explain possibilities and limitation of AI systems, and examine technological and ethical perspectives on AI. 

This course offers a coherent and broad introduction to AI and a small project with the aim to apply AI solutions to a real world problem proposed and supervised by researchers from the TU Delft AI Labs [link: https://www.tudelft.nl/ai/tu-delft-ai-labs ]. This is one whole 15EC course that consists of course work and a project where you develop an AI solution and apply your knowledge. Available projects will be announced starting in June.

You will be offered a broad introduction to topics such as: Data engineering, Ethics in AI, Machine Learning, Unsupervised Learning, Neural Networks, Natural Language Processing, Computer vision, Robotics. 

The setup of this AI elective consists of a blended design whereby the flipped classroom approach is used. Students are offered online video lectures, papers, quizzes to prepare themselves. The day after they have an on-campus session with teaching staff where they dive deeper in the material collectively.

 This course aims at MSc students from all faculties of TU Delft with no to little prior AI knowledge and those that want to explore the field of AI within their own domain. For example, an architecture student that wants to improve their AI knowledge to formulate an AI solution within the architecture domain.

  • Prerequisite knowledge: basic programming skills and basic knowledge of statistics. Knowledge of linear algebra is a plus.
  • This course is not accessible for computer science and robotics students because it has too much overlap with their programmes.

Information academic year 2024-2025:

15EC course
Start date: 2 September – 8 November 2024 (during Q1)
Application Procedure: you can now enroll through Project Forum and indicate your preference for a project.

Enroll here: https://projectforum.tudelft.nl/course_editions/108

Teaching staff

Seyran Khademi

ABE, AiDAPT Lab
Responsible lecturer and course coordinator
Computer vision

Arkady Zgonnikov

ME, HERALD Lab
Project coordinator

Myrthe Tielman

EEMCS, AI*MAN Lab
Introduction to AI, MAS

Emir Demirović

EEMCS, XAIT Lab
Data Engineering

Jazmin Zatarain Salazar

TPM, HIPPO Lab
Ethics in AI

Christoph Lofi

EEMCS
DBMS

Ujwal Gadiraju

EEMCS, Design@Scale Lab
Data work, Interpretable & explainable AI

Jie Yang

EEMCS, Design@Scale Lab
Data work, Interpretable & explainable AI,  NLP

Avishek Anand

EEMCS
NLP

Junzi Sun

AE
Combinatorial Optimisation

Stefan Buijsman

TPM
Multi objective decision support & Ethics in AI

Juan Manuel Duran

TPM
Multi objective decision support & Ethics in AI

Tom Viering

EEMCS
Machine Learning

Hanne Kekkonen

EEMCS, SLIMM Lab
Regularization

Alexander Heinlein

EEMCS
Ranking for recommender systems

Alfredo Nunez Vicenco

CEG
Unsupervised Learning

Hongrui Wang

CEG, Faculty member TU Delft AI Labs
Unsupervised Learning

Wendelin Böhmer

EEMCS, BIOLab
Reinforcement learning

Luca Laurenti

ME, HERALD Lab
Neural Networks

Amira Elnouty

EEMCS
Dimensionality reduction

Martin Klomp

ME
Robotics

Pierre Mercuriali

ME
Symbolic AI & Knowledge Representation

Ibo van der Poel

TPM