Master's Programme in ICT Innovation (EIT Digital Master School) - Autonomous Systems
Ansökningsperioden har slutat
The Autonomous Systems (AUS) study programme is a combination of computer science and electrical engineering. During the programme, students will gain new skills in both areas. In computer science, relevant skills include Internet of Things (IoT), machine learning, artificial intelligence and robot vision. In electrical engineering, relevant skills are automation, robotics, control, embedded systems and communications.
Students learn the latest theoretical knowledge and know how to apply their skills in practical real-life problems. Typical application areas of autonomous systems include autonomous vehicles, intelligent robots, industrial IoT and autonomous software systems.
At the moment, the job market looks very promising. Automotive manufacturing is a major industry in Europe and the university partners are well connected with these companies. In addition, autonomous systems are relevant in service applications and in manufacturing, logistics, shipping, mining, and recycling industries. In addition, autonomous software systems are becoming widespread in application areas, such as media, finance, customer service, and healthcare.
In the Master’s thesis project, the students will work with the industry partners or the active startup community around the Helsinki node of EIT Digital.
Language of instruction
The language of instruction of the programme is English.
The programme is a double degree programme, which means that you will study at two European universities and in two different countries. The programme has an integrated technical content (Technical major) and business content (Innovation & Entrepreneurship – I&E minor). The I&E minor is shared between all programmes. An integral part of the second year is a Master’s thesis (30 ECTS).
The double-degree programme is implemented jointly by leading European universities. The students can select their favorite first (entry) and second (exit) year universities. Each university has a unique specialisation area, which allows students to select their own second year area of expertise.
Structure of the studies
Please note that both ENTRY and EXIT universities might be subject to changes. Kindly check before applying.
During the first year, a similar set of courses are offered at the four involved entry points Aalto University, TU Berlin, UniTN - Trento and KTH. This includes robotics, artificial intelligence (or machine learning), modelling, estimation and control. In addition, some elective courses may be chosen to prepare for a specialisation. An important part of the programme are also the Innovation and & Entrepreneurship (I&E) courses.
During the second year of studies, the student chooses a spesialisation option offered in one of the six exit point universities: Aalto University, KTH, TU Berlin, UniTN - Trento, Eurecom or ELTE. The specialisation option offered at Aalto University is Robotics and Artificial Intelligence.
- The courses of the Robotics and Artificial Intelligence specialisation combine both electrical engineering and computer science. Elective courses include autonomous mobile robots, micro- and nano robotics, computer vision, robotic vision, and machine learning. The students will learn how to build autonomous, intelligent robots and robotic systems.
Specialisation courses (20 ECTS) can be selected from the following list:
- Stochastic models and estimation (5 ECTS)
- Model-Based Control Systems (5 ECTS)
- Reinforcement learning (5 ECTS)
- Wireless Systems (5 ECTS)
- Software Design and Modelling (5 ECTS)
- Computer Vision (5 ECTS)
- Deep Learning (5 ECTS)
- Kernel Methods in Machine Learning (5 ECTS)
- Bayesian Data Analysis (5 ECTS)
See the Autonomous Systems curriculum at Aalto University on the student portal Into.
Admission to the EIT Digital Master School majors
The ICT Innovation programme has separate application periods and admission procedures from the rest of the Master's programmes offered by Aalto University. Further information on the admission is available on the EIT Digital Master School website for prospective students (masterschool.eitdigital.eu).
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