KDK2010 Art, Dissemination and Experiments Course description

This course provides a broad introduction to Artificial Intelligence (AI), with methodologies and techniques that can be applied to different application domains. The course will balance theoretical approaches and practical tasks. Two broad AI areas will be covered, namely supervised and unsupervised methods. Among those, standard methods for regression, classification and clustering will be covered, e.g. support vector machines, nearest neighbor, decision tree, K-means, agglomerative and hierarchical clustering. An introduction to the usage of artificial neural networks and backpropagation algorithm will be provided.

Basic programming skills (C, Python, Java, or similar programming language)

Discrete mathematics course at undergraduate level

None.

After completing this course, the student has the following learning outcomes, defined as knowledge, skills and general competence:

Knowledge

The student:

• has basic knowledge of relevant processes and methods related to artistic work
• has insight into art history, art and dissemination theory, as well as philosophical aesthetics

Skills

The student:

• can explore and experiment in various artistic processes
• can select and use suitable strategies in artistic, written, and oral communication
• can disseminate art to a selected target group
• can use artistic tools and relevant theory in own projects

General competence

The student:

• has knowledge of diversity and multicultural perspectives
• has insight into copyright matters related to the subject area
• has knowledge of ethical challenges affecting the subject area

The course is organized in the form of artistic work and theoretical studies. Project work is an essential part of the study. Teaching takes place with an emphasis on progression, from common basic tasks to independently chosen topics. Presence in the workshop and active participation in the teaching are required. The course uses teaching methods such as lectures, seminars, supervision and debates. The student is expected to obtain relevant subject matter beyond the syllabus and is encouraged to use the learning centers and the resources offered there.

The course consists of lectures and seminars on techniques and methods, as well as a project to be carried out in groups of 2 to 4 students. The project will be chosen from a portfolio of available problems, either from industry partners or by the research groups. The students will work in groups and will submit an academic report and give an oral presentation. Lab sessions supporting the assignments will be provided.

3 compulsory assignments done in groups of 2-4 students must be approved in order to be admitted to the final exam.

1. 30% of the grade based on the academic report (in groups of 2-4 students, 5-10 pages written report with link to code e.g. on github).
2. 70% of the grade based on individual written examination (3 hours).

Both exams must be passed in order to pass the course.

The exam result can be appealed.

No support materials are allowed for the written exam.

Grade scale A-F.

Two examiners. External examiner is used periodically.