Clustering and Unsupervised Methods in Machine Learning

Unsupervised learning techniques, particularly clustering, are essential for discovering hidden patterns and structures in data. In this interactive workshop, participants will explore key unsupervised learning methods, including k-means clustering, hierarchical clustering, and density-based clustering. The session will also introduce dimensionality reduction techniques, including principal component analysis (PCA) and t-distributed stochastic neighbour embedding ( t-SNE ) for visualisation and feature extraction. Through interactive coding exercises in Python (Scikit-Learn, NumPy, Pandas, Matplotlib, Seaborn), attendees will apply clustering techniques to real-world datasets and learn how to evaluate the quality of clustering results. The workshop will also discuss applications of clustering in different domains, from customer segmentation to anomaly detection. By the end of the session, participants will have the practical skills to apply clustering and unsupervised learning methods effectively in data analysis. No prior machine learning experience is required, but basic Python and statistics knowledge are recommended.

This workshop is ideal for researchers, data analysts, and professionals looking to apply clustering techniques to explore and structure large datasets without labelled outputs.

By the end of this workshop, participants will be able to: 

• Understand key concepts in unsupervised learning, including when to use clustering and its applications. 

• Compare different clustering techniques and understand their strengths and weaknesses. 

• Apply clustering algorithms using Python and relevant libraries to analyse real-world datasets. 

• Evaluate clustering results using metrics, such as silhouette score, inertia, and Davies-Bouldin index. 

• Perform dimensionality reduction using PCA and t-SNE to improve clustering performance and visualisation.