OEKB100148 Data-driven AI

Art
prüfungsimmanente Lehrveranstaltung
Semesterstunden
3
Vortragende/r (Mitwirkende/r)
Holzinger, Andreas , Schweng, Stefan , Weibel, Jean-Baptiste
Organisation
Waldwachstum
Angeboten im Semester
Wintersemester 2025/26
Unterrichts-/ Lehrsprachen
Englisch
Lehrinhalt

The course Data-Driven AI introduces foundational principles of current artificial intelligence (AI), which is now worldwide on everyone's lips, with a focus on computational thinking and low-code tools for solving real-world problems in forestry. Students will learn how to frame forestry-related challenges as data-driven tasks, explore key AI concepts such as pattern recognition, classification, and decision support, and apply user-friendly platforms to prototype solutions. Emphasis is placed on interpretability, sustainability, and responsible AI use in ecosystem management. No prior programming experience is required.

Topics covered in this course:
-) Introduction to ML
-) Data structures, physics of data, data collection and cleaning
-) Linear/Ridge Regression, Supervised ML, Generalization, Regularization and Overfitting
-) Classification and Metrics
-) Training and Optimization
-) Unsupervised (K-Means, etc.)
-) ML Models (Neural Networks, Decision Trees, SVM, etc.)
-) Deep Learning (CNNs, LLMs, etc.)
-) Explainability in AI
-) Introduction to AutoML
Inhaltliche Voraussetzungen (erwartete Kenntnisse)

Students should be familiar with simple mathematical concepts and have a general understanding of how to use a computer. A basic curiosity for AI is helpful. The course is designed to be beginner-friendly and will guide students step by step through the content to prepare students for the future of data-driven AI for digital transformation in smart forestry.
Lehrziel

Students will acquire foundational knowledge in data-driven artificial intelligence (DDAI) with a focus on applications in smart forestry. Students will develop an understanding of basic mathematical and computational concepts relevant to AI. Through guided, step-by-step instruction, students will gain practical skills in using digital tools for data analysis and AI model implementation. By the end of the course, they will be able to interpret and apply core AI techniques to support digital transformation processes in forestry, fostering critical thinking and curiosity toward responsible and human-centered AI use in environmental domains. This course is in English only.
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