AI4all
AI4all offers all students of TU Wien a foundation in Artificial Intelligence and Data Management. Explore how AI methods can be applied across science and engineering effectively and responsibly.
Artificial Intelligence is transforming research, science, and engineering. AI4all aims to make essential knowledge about artificial intelligence and data management accessible to students across disciplines, giving them the knowledge and skills to understand different AI approaches—from symbolic AI, which represents knowledge through rules and logic, to sub-symbolic AI, which learns patterns from data—and apply them to challenges in their own fields.
The program offers two foundational courses by the Faculty of Informatics, providing the basis for the discipline-specific courses offered by the Faculties of Technical Chemistry, Civil and Environmental Engineering, and Mechanical and Industrial Engineering. The foundational courses are offered in the winter term, the discipline specific courses in the summer term.
AI4all brings together the foundations of AI with practical applications, helping students understand how AI methods work, how to use them effectively, and how to develop or adapt AI tools to address discipline-specific challenges. Throughout the program, responsible AI remains a central consideration, with topics such as ethics, fairness, transparency, and responsible data use forming an integral part of understanding and applying these technologies.
Foundational Courses
The Essentials of AI
The Essentials of AI forms the core of the AI4all lecture series. Throughout the course, you’ll develop a basic theoretical and practical understanding of artificial intelligence. By the end of the course, you’ll understand how standard machine learning algorithms and large language models (LLMs) are built. Beyond machine learning, the course introduces fundamental AI concepts such as problem solving, heuristic search, and automated planning. Through hands-on exercises, you’ll learn how to build and use these tools in practice.
Data Management for Science and Engineering
Data Management for Science and Engineering introduces the fundamentals of managing and processing data in science and engineering, with no prior computer science background required. Throughout the course, you’ll learn how to structure, store, query, and work with data throughout its lifecycle, from designing data models and databases to practical data processing. Through hands-on exercises, you’ll apply the concepts from the lectures and develop practical skills for working with data in scientific and engineering contexts.
Discipline-Specific Courses
AI for Chemistry
This course introduces artificial intelligence methods for modeling and understanding chemical systems. You’ll learn how AI can be used to work with different types of chemical data, from molecular structures and sequences to dynamic processes. You’ll explore how machine learning can help represent, analyze, and predict the behavior of chemical systems, as well as how AI can be used to develop new molecular models and materials. The course also introduces modern generative approaches that can support the discovery and design of molecules. By combining theory and practice, you’ll learn how AI is transforming research in chemistry and related fields.
ML in Civil and Environmental Engineering
This course explores how artificial intelligence can be applied to real-world challenges in energy, environmental, and civil engineering. Throughout the course, you’ll learn how AI can be combined with knowledge from the physical world to better understand and predict complex systems. You’ll work with real-world data, including data that changes over time, and explore how uncertainty can be taken into account when using AI. The course also addresses practical aspects of reproducibility and transparency. Each lecture combines a theoretical lecture with hands-on exercises, giving you the opportunity to apply what you learn.
AI for Mechanical and Industrial Engineering
This course introduces the fundamentals of artificial intelligence and its application to mechanical and industrial engineering. You’ll gain a practical understanding of how AI can be used in areas such as manufacturing, robotics, and cyber-physical systems. You’ll learn how data-driven approaches can be used to understand, model, and improve complex engineering systems. You’ll explore how machine learning can complement traditional engineering knowledge by learning from data. The course combines scientific foundations with practical applications, helping you understand how AI can complement traditional engineering approaches.
Contact
You have a question about AI4all? Contact our Professors and Experts:
Milosh Jovanovikj M. Jovanovikj
Data Management for Science and Engineering
