Master of Engineering in AI-Integrated Engineering
The Master of Engineering in AI-Integrated Engineering is a graduate program designed to empower professionals with both domain-specific engineering expertise and applied artificial intelligence (AI) capabilities.
This program is open to graduates from all disciplines, not just computer science or computer engineering. It is structured to be completed within 2 years and is especially ideal for students coming from the 4+1 academic track at Kasetsart University. With prior exposure to AI foundations and research integration during their undergraduate senior project, students from this track can fast-track through the program without repeating fundamental training.
Key Features
- AI-Driven Specialization: Students apply AI techniques—such as machine learning, data science, and computer vision—to real-world problems rooted in their engineering domain.
- Theoretical and Practical Learning: Although the program emphasizes practical application, students also gain a strong conceptual foundation and learn to integrate AI with their engineering expertise.
- Suitable for all Engineers, but Optimized for 4+1: While students from diverse academic backgrounds are welcome, the course is specifically designed for those with an engineering foundation. Participants from Kasetsart University’s 4+1 track are especially well-prepared to dive into advanced integration and research from the outset. Those with limited exposure to AI or programming will begin with preparatory courses in foundational AI and practical coding.
Graduation Requirements
To complete the degree, students must:
- Conduct a research project relevant to AI in engineering
- Demonstrate the ability to integrate AI with domain knowledge and Publish at least one peer-reviewed article or conference paper
Career Opportunities
Graduates will be equipped to:
- Work as Data Analysts, AI-empowered Engineers, or Smart System Developers
- Return to their original engineering careers, now empowered with AI thinking and tools
- Contribute to interdisciplinary innovation teams in both industry and academia
Curriculum Overview
The program consists of core AI courses and elective courses that allow students to deepen their expertise in various areas. Students will also conduct applied research in collaboration with the department’s diverse laboratories within the Faculty of Engineering, enabling them to produce and publish high-quality research.
The curriculum will have the following structure.
| Courses | Credits |
|---|---|
| 1. Major courses | minimum 24 credits |
| - Seminar | 2 credits |
| - Major requirements | 7 credits |
| - Major electives | minimum 15 credits |
| 2. Thesis | minimum 12 credits |
Courses
Major required courses
- AI and machine learning courses (1-credit courses)
- Advanced machine learning I
- Advanced machine learning II
- AI for data interpretation
- Data engineering courses (1-credit courses)
- Data acquisition
- Data preprocessing
- Database and data warehouse
- Research methods in computer engineering
Major electives are divided into three groups, with example courses such as:
- AI and machine learning courses (1-credit courses)
- Deep learning I
- Deep learning II
- Foundation of generative AI for engineering
- Advanced applications of generative AI for engineering
- AI for image analysis
- Pattern recognition
- Applied optimization for AI
- Planning and scheduling
- Evolutionary learning
- Ethical AI
- Fair machine learning
- Explainable AI
- AI security
- Data engineering courses (1-credit courses)
- Data visualization
- Data science and analytics
- Complex-data processing
- Advanced data encoding
- Digital image processing
- Specialized courses
- AI-Enhanced manufacturing processes
- AI in robotics
- Digital twin technology
- Predictive maintenance and condition monitoring
- Computational material science
Whether you’re starting fresh or continuing from 4+1, this program transforms you into a professional who can think, work, and lead in the age of AI.