Degree structure
Source: https://www.tue.nl/en/education/graduate-school/master-artificial-intelligence-and-engineering-systems/degree-structure Parent: https://www.tue.nl/en/education/graduate-school/master-artificial-intelligence-and-engineering-systems
The NVAO accredited master program AI&ES in 2021 as a unique program that responds to the industrial, societal, and academic needs to apply AI to solve engineering problems in an integral way. Students follow an exciting, high-quality program based on TU/e’s strong educational and research programs.
Program structure
The AI&ES master's program consists of 120 EC (European Credits) spread over two years, designed to balance theoretical learning and practical application.
| Year | 1st |
| Components | Core program |
| ECTS | 30 |
| Year | 1st & 2nd |
| Components | Specialization Elective Program |
| ECTS | 15 |
| Year | 1st |
| Components | Team Internship |
| ECTS | 10 |
| Year | 1st & 2nd |
| Components | Free Electives Program (Courses & Internship) |
| ECTS | 15 |
| Year | 2nd |
| Components | Graduation project |
| ECTS | 45 |
| Year | Components | ECTS |
|---|---|---|
| 1st | Core program | 30 |
| 1st & 2nd | Specialization Elective Program | 15 |
| 1st | Team Internship | 10 |
| 1st & 2nd | Free Electives Program (Courses & Internship) | 15 |
| 2nd | Graduation project | 45 |
Core Courses
Students gain a solid foundation in AI and engineering systems, covering:
- Data science and Learning in AI: Data Analysis and Learning Methods
- Human-AI interaction and ethics: Human and Ethical Aspects of AI
- Programming: Software Engineering for AI
- Mathematics: Stochastic Processes, Filtering and Estimation
- Optimization: Engineering Optimization
- Engineering Systems: Physical and Data-Driven Modelling course and Control principles for Linear Systems course.
Free elective courses
Customize your degree with courses in AI, engineering, entrepreneurship, or interdisciplinary topics in consultation with the track mentor.
Team Internship
Work on a real-world challenge in a small interdisciplinary team, integrating AI with engineering for an innovative solution.\ Examples of challenges:
- AI for Autonomous Farming
- Machine Vision Inspection of Wind Turbines
- Personalized Driver Feedback
- Soft Robotics
- Perception System Development and Deployment for Automated Driving
- Image-guided therapy challenge
- AI-based digital twins for solar energy systems in urban areas
- Data-driven reduced order modelling for non-Newtonian fluids mechanics
- Development of an autonomous thermal control system for clean rooms using integrated machine learning
- Teaching robots how to learn
- Analyse and predict output of 15 (similar) heat ovens and production in rooftile factory in Teteringen
- Analyse parameters influencing quality rejects of cheese production line
International Experience
You have the possibility to follow courses abroad, the Electrical Engineering department has bilateral agreements with partner universities around the world. Spending some time abroad is a great way to immerse yourself in another culture, develop soft skills like (intercultural) communication, adaptability and problem-solving, and strengthen your position on the job market. Students may also follow a specialization course at another university if this gives a better match with their study plans.\ Additionally you have the possibility to do your graduation project outside of the Netherlands.
Graduation project
The Graduation project AI&ES is an individual research project worth 45 ECTS credits on a topic related to artificial intelligence in the context of engineering systems. The graduation project can be carried out within all TU/e departments connected to the AI&ES master, within a company or another university or research institute in the Netherlands or abroad. The Graduation project is Track related, and during 8 months, you will further develop your expertise and skills.\ \ Some examples of the Graduation projects that students have conducted relevant for each Track within the AI&ES master:
- Multi-Agent System Planning for Risk-Aware Airport Ground Handling Using Deep Reinforcement Learning
- Disruption Management for Electric Buses
- Sensor Processing based on Oscillatory Neural Networks for Edge AI
- Towards explainable transformers for 3D medical image analysis
- Magical planning: optimizing power grids at the click of a button (with eRoots)
- Innovative AI techniques for managing crowd flows at F1 Races and Festivals
- Cracking the DNA Code of Materials
- Data Mining and Analytics at MSD Animal Health
Curriculum
The Education guide AI&ES offers a complete overview on how the program is organized.
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Freek Klabbers