Admission requirements
Assumed/recommended prior knowledge
It is strongly recommended that students:
have familiarity with neural networks and deep learning frameworks
are interested in art and creative practices
have proficiency in Python
Description
This course examines the evolving intersection where machine learning meets artistic expression. AI is increasingly present in daily life—and art is no exception, from museum installations to machine-driven aesthetics. Today’s AI systems not only generate images, music, and poetry, but also critique artistic work—bringing new debates about creativity, authorship, and aesthetic judgment. These developments spark both excitement and skepticism, raising important philosophical and ethical questions: What does it mean for a machine to create or critique art? And more fundamentally, what is art? Blending different perspectives, we will discuss the place of AI in the art world.
This course teaches the models and methods behind AI-generated art: how they work, why they matter, and their potential future directions. We will examine the technical background of generative systems, along with their limitations and implications. In addition, we will discuss the questions of authorship, ethics, and the broader cultural impact of machine-generated art. This course blends theory, practice, and critical reflection across both technical and artistic dimensions. By the end of the course, you should have acquired a solid understanding of the intersection between AI and art.
Course objectives
After the successful completion of this course, the students should be able to:
Understand the core principles behind AI-generated art
Compare and evaluate different generative techniques
Trace the evolution of AI in art, from the early algorithms to state-of-the-art models
Employ AI models in a range of artistic applications, from generation to evaluation
Experiment with AI models and interpret their outcomes in relation to perception, aesthetics, and artistic intent
Critically position the role of AI in the art world and its relationship to human creativity
Connect theoretical knowledge to real-world applications
Timetable
The most recent timetable can be found at the Computer Science (MSc) student website.
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Lectures, practical classes, research paper seminars, assignments, and a final project. Students will receive feedback on both presentations and written reports.
Course load
Hours of study: 168 (= 6 EC)
Lectures: 26
Practical assignments: 56
Final project: 50
Exam and preparation: 36
Assessment method
Grading is based on:
Written individual exam (closed book): 30%
Assignments: 25%
Final project: 30%
Project presentation: 15%
The final grade is the weighted average of the above components.
If an assignment or a presentation is not completed, the resulting grade is a 0. There will be no retakes for the assignments, final project, and the presentation. Students have the opportunity to retake the written exam. The final grade can only be sufficient if the weighted average grade is at least a 5.5.
To pass the course, students must complete the final project and presentation.
Completed assignments are valid for one year. If a student fails the course, all components—including the assignments—must be completed again the following year.
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
Reading list
Study materials will be provided by the lecturer during the course.
The following literature is recommended but not mandatory for this course:
S. Audry (2021). Art in the age of machine learning, The MIT Press.
A.I. Miller (2019). The artist in the machine, The MIT Press.
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Lecturer: dr. Derya Soydaner
Teaching assistants
Remarks
Elective, external and exchange students (other than Computer Science master and Creative Intelligence and Technology students) need to be admitted to the course before registration due to limited capacity. Contact the programme coordinator to request admission; include a short description of your course interest and state your current study programme in your correspondence.
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.