|Search for study unit:||ECTS||1||2||1A||1S||2A||2S||1||2||3||4||5||1||2||3||4||5|
ADVANCED PROFESSIONAL STUDIES
|Research methods and scientific writing||5|
|Dynamic systems – modeling, simulation and control||5|
|Machine learning methods in automation||5|
|Development of modern automation systems||5|
|Project course in automation||5|
MASTERS DEGREE THESIS
|ECTS credits per period / semester / academic year||30||30||15||15||15||15||15||15||10||10||10||15||15||10||10||10|
Due to the timing of optional and elective courses, credit accumulation per semester / academic year may vary.
The Master’s degree programme (part-time studies, 2 years) in Automation Technology - 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 control systems. The students will study automation and control both on a theoretical level and on a practical level – learning to design, test and implement intelligent control, supervisory and automation systems. A special emphasis is put on using modern methods of machine learning and decision making in the development of sustainable applications in control and automation.
The programme 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 May 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.
The student gains knowledge of how to model, control and simulate common industrial processes. The student can identify opportunities for industrial decision-making and learn how to implement intelligent solutions. The student learns 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 develops an understanding of the ethical, sustainability and security challenges of modern industrial automation systems.