Search for study unit:	ECTS	1	2	1A	1S	2A	2S	1	2	3	4	5	1	2	3	4	5
ADVANCED PROFESSIONAL STUDIES
Introduction to intelligent systems	5
Dynamical systems	5
Machine learning methods	5
Development of intelligent automation systems	5
Industrial artificial intelligence	5
Machine sensing project	5
		30	0	15	15	0	0	15	15	10	10	10	0	0	0	0	0
MASTERS DEGREE THESIS
Master's Thesis
Master's Thesis - part 1	10
Master's Thesis - part 2	10
Master's Thesis - part 3	10
		0	30	0	0	15	15	0	0	0	0	0	15	15	10	10	10
ECTS credits per period / semester / academic year		30	30	15	15	15	15	15	15	10	10	10	15	15	10	10	10

Description

The Master’s degree program (part-time studies, 2 years) in Intelligent Systems is designed for the professional engineer who wants to deepen his/her knowledge and understanding of the methods and possibilities of modern automation and intelligent systems. The students will study the area of intelligent systems, machine learning, artificial intelligence and industrial automation both on a theoretical level and on a practical level – learning to design, test and implement solutions for intelligent systems. A special emphasis is put on using modern methods of machine learning and data-driven decision making in the development of sustainable applications in industrial automation.

The program consists of degree specific professional studies (30 ECTS) and a Master’s thesis (30 ECTS). The studies are designed for students who are already working. During these two years, the studies are scheduled for two days a month from September to June at Campus Vaasa.

Sustainable development is the foundation of our future. Therefore, issues related to sustainability are included in many of our courses, sometimes as central content, sometimes as a setting. From UN's 17 goals for sustainable development, number 7 (Affordable and clean energy), 9 (Industry, innovation and infrastructure), 11 (Sustainable cities and communities) and 12 (Responsible consumption and production) are paid attention to in the education.

Objective

Industrial automation is undergoing rapid development due to digitization and technological improvements. The student will gain knowledge of how to model and simulate industrial processes. The student can identify opportunities for industrial decision-making and learn how to implement intelligent solutions. The student will learn how to apply machine learning methods to solve industrial problems by using classification, regression, detection, and optimization. The student understands the importance of thorough testing and evaluation of models and of high-quality data to succeed with the data-driven decision-making process. The student also develops an understanding of the ethical, sustainability and security challenges of modern industrial automation.