Master's Programme in Data Science is responsible for the course.
The course belongs to the Advanced studies / Computers and cognition module.
The course is also available in Basic studies in Data Science.
The course is available to students from other degree programmes.
Prerequisites in terms of knowledge
Good programming skills, basics of probability calculus and elementary knowledge of machine learning.
Prerequisites for students in the Data Science programme, in terms of courses
DATA11001 Introduction to Data Science (or DATA11002 Introduction to Machine Learning)
Prerequisites for other students in terms of courses
TKT10002 Introduction to Programming and TKT10003 Advanced Course in Programming (for good programming skills), Probability Calculus or Introduction to Statistics, DATA11001 Introduction to Data Science (or DATA11002 Introduction to Machine Learning)
Recommended preceding courses
DATA11002 Introduction to Machine Learning
Introduction to Artificial Intelligence and Introduction to Machine Learning complement the overview of modern intelligent systems.
After completing the course, the student is able to
- explain the difference between mere generation and creativity
- analyse a given generative system conceptually using various models of computational creativity
- design and implement systems that create novel artifacts using generic methods
- understand the limitations and strengths of the methods, argue for decisions taken
- evaluate creative systems
Recommended time/stage of studies for completion: first or second year
Term/teaching period when the course will be offered: Autumn, second period
Computational creativity is the study of creative behavior by computational means. This includes machine creativity, i.e., creative computers, as well support of human creativity by computational means, and study of computational creative process.
The course is an introduction to central concepts and models of computational creativity, in particular machine creativity: different types of creativity, formal models of creativity, architectural issues of computational creativity, and evaluation and analysis of computational creativity. Some practical example methods for computational creativity are also covered, in creative fields such as poetry, music and images.
The course literature consists of selected original papers in computational creativity. The list of material is provided with each instance of the course.
The course uses teaching methods based on active learning. During the course, students apply and develop their skills by applying them on given tasks and reporting on their results and learning. The course requires independent work on given individual and group assignments and participation in selected teaching sessions used for exercises and group work. Depending on the need, a supervised workshop/lab session can be organised for working on the tasks; participation is then recommended (but not mandatory) since it can be more fun to study and work together than individually, and getting feedback from peers and tutors is useful for learning.
The assessment is based on three components: quality of the course project work, individual contribution to the project and a course exam (which can be a take-home exam). At least 50% of available points must be obtained from the project work and the exam. Attendance to the contact teaching may be rewarded as course points.
The grading scale is 1-5.
General exams last 3 hours and 30 minutes. Renewal exam (marked with "(U)") is the first general exam after the course and also a renewal exam of course exam(s). In a renewal exam the points student has earned during the course are taken into account. Exams marked with "(HT)" are allowed only to students who have completed the obligatory projects or other exercises included in those courses. Exams marked with "(HT/U)" are renewals to students who have completed the obligatory projects during the course. General exams might cover different area than the lectured course. Check the course web page and contact the responsible teacher if in doubt.
The course contains contact teaching, exercises, a small course project (done in groups) and an exam. To pass the course, a student has to complete the course project, be active in the group work and obtain at least 50% of points available from the exam. Group work is essential for the course and contact teaching attendance is strongly recommended.
Separate exams will also be offered, based purely on written material and not requiring attendance in the course.