AI course for Artists – Λi

The AI for artists course for artists is a free course but substantial training via multiple sessions. It’s a good opportunity to learn how to use techniques from AI to create art, it focus on both the basic knowledge in artificial intelligence and the application of creative ideas arising from art, using an interdisciplinary format. This course for artists not only offers teaching sessions but also includes workshops with case studies and assignments.

Instructors : Sana Nouzri, Egberdien van der Torre and invited speakers

There were two sessions of AI for Artists course

Session 1: AI for Artists course has been started on October 16th 2020 and focused on both the basic knowledge in artificial intelligence and its practical everyday applications and especially the creative tools to generate AI Art, using an interdisciplinary format.

Session 2: A second round of AI for artist course has been started on February 26th, 2021, and a new group of artists have been invited and registered to follow the same program.

Location: Partly on the Belval campus (depending on university rules),

Next term of the course will be organized soon.


	Session content	Workshop
Session 1: Introduction and Overview of AI	• Introduce artists and teaching team. • Course Introduction, why AI and Art? • Appetizer • Start session 1: Provide a comprehensive and detailed knowledge of Artificial Intelligence concepts.	Workshop 1: Search and problem solving with AI, Classic and Modern AI methods.
Session 2: Machine Learning • part 1- Understanding and Visualizing Data	• Overview of the primary building blocks of machine learning • Learn the basic characteristics of data sets and identity effective statistical tools and visualizations to gain insights from data (Art_Materials Prices data set).	Workshop 2: Explore and visualize data, complete an exercise on “Gaining Insights from Data”.
Session 3: Machine Learning • part 2-Prediction (Regression, Classification)	• Understand the basic concept of linear regression and how it can be used with historical data to build models that can predict future outcomes. • Classification is used to predict outcomes that fall into two or more categories, such as: male/female, yes/no, or red/blue/green.	Workshop 3: Regression and Classification Analysis by using a provided data set,
Session 4: Deep learning – Neural Networks	• Neural networks are much like the networks in the human brain. They are used in machine learning to model complex relationships between inputs and outputs and to find patterns in data. • Decision making (Reinforcement learning) is about selecting the “optimal” decision or action in the presence of uncertainty, which all professionals face regularly.	Workshop 4: • Neural Networks Analysis by using a provided data set, complete an exercise on neural network architecture. • Decision Making Analysis by using (Q Learning)
Session 5: Time line overview of AI & Art technology	An ongoing timeline-based Art & research project on the evolution of the Art, Science, Technology and Society system by Yolanda- Spinola-Elias (Visual Artist and Prof at university, span).
Session 6: Deep learning – Neural Networks (GAN) part 1	Introduction of Generative Adversarial Network (GAN)	Workshop 5: Use Creative Tools to Generate AI Art (AI Generated Cartoons) Download and use source code from GitHub or other sources for creating Art generated by GANs
Session 7: Deep learning – Neural Networks (GAN) part 2	Generative Adversarial Network (GAN)	Workshop 6: Use Creative Tools to Generate AI Art (AI Generated Images / Pictures) Use Creative Tools to Generate AI Art (AI Generated Music/Sound) Download and use source code from GitHub or others sources for creating Art generated by GAN
Session 8: Generative Art	Generative Adversarial Network (GAN) Generative Art is a process of algorithmically generating new ideas, forms, shapes, colors or patterns	Workshop 7: Use Creative Tools to Generate AI Art (AI Generated Dances/ Movements) Generative Art: Examples, Software and Tools to Make Algorithm Art
Session 9: Computer Vision	Introduction to Computer vision by Dr Kassem Al Ismaeil (Ph.D. Associate Researcher & Project Manager at SnT, university of Luxembourg)	Workshop 8: Describe a project using the Machine Learning Building Blocks described during the course (Part 1 + Discussion).
Session 10: Reflections on AI & Art	• Bias and AI • Fairness and AI • Ethics and AI	Workshop 9: • Describe a project using the Machine Learning Building Blocks described during the course (Part 2 + Discussion)