Two-Day Workshop on Unsupervised Machine Learning with Applications in Agricultural Science Using R

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.

Leader: Reza Belaghi,  Department of Energy and Technology, Unit of Applied Statistics and Mathematics, SLU 

Online by Zoom

Outline

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.