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This workshop will explore essential techniques in unsupervised learning, focusing on factor analysis, principal component analysis (with application in high-dimensional regression), and clustering methods, with their applications in agriculture, natural, and animal sciences. Designed for researchers, PhD students from the Swedish Agricultural University, the workshop will provide both theoretical foundations and practical applications using R software.
Participants will gain hands-on experience with real-world datasets, learning how to analyze complex data, reduce dimensionality, and uncover patterns using unsupervised AI. By the end, attendees will be equipped to apply these techniques in their own research fields.
Prior knowledge of basic statistics is recommended.
Day 1 (Feb 13; 9:15-12:00):
Introduction to Unsupervised Learning and its applications in agriculture
Factor Analysis: Theory, assumptions, and interpretation
Principal Component Analysis (PCA): Dimensionality reduction and visualization
Hands-on session: Applying PCA to real datasets
Day 2 (Feb 14; 9:15-12:00):
Clustering techniques: k-means, PAM, hierarchical clustering, and DBSCAN
Hands-on session: Clustering and interpreting results in real-world applications
Questions, Answers and Discussions.
Leader: Reza Belaghi, Department of Energy and Technology, Unit of Applied Statistics and Mathematics, SLU