CCBRE Scientific Seminar: Practical AI for Real-World Problem Solving

Artificial Intelligence and Machine Learning are no longer abstract research topics—they are powerful tools for addressing real, complex challenges in industry, the environment, and society. This course is designed to bridge the gap between theoretical AI concepts and practical, deployable solutions.

“Practical AI for Real-World Problem Solving” introduces participants to the complete machine-learning workflow using Python, with a strong emphasis on hands-on experience, real datasets, and interpretability. Rather than focusing on mathematical formalism alone, the course demonstrates how AI systems are designed, trained, evaluated, and used in practice—from raw data to actionable insights.

Participants will work with environmental, biomass, waste-management, and remote-sensing datasets, reflecting real challenges addressed by the Centre for Cleantech and Biomass Resource Efficiency (CCBRE). Core topics include data preprocessing, feature engineering, supervised learning (classification and regression), unsupervised learning (clustering and Principal Component Analysis), model evaluation, and the responsible use of AI outputs for decision-making.

By the end of the course, participants are able to:

• understand how AI models learn from data,
• design robust and practical machine-learning pipelines,
• critically evaluate and interpret model results,
• apply AI methods to real-world sustainability and industrial problems.

The course is suitable for engineers, researchers, decision-makers, and professionals who want to move beyond buzzwords and gain practical, transferable AI skills with immediate real-world relevance.