GRNET announces, in the context of SmartAttica EDIH (European Digital Innovation Hub), the 8th Module of Τraining modules for SMEs with the subject "Unsupervised Learning".
Date: June 16th, 2025, at 12:00 EET
Location: Online via Zoom
Presentation Languages: Greek
Instructor: Dr. Nikolaos Bakas (GRNET), Panagiota Gyftou
Description: Join us for an insightful seminar on Unsupervised Learning, where we continue into the world of clustering and dimensionality reduction techniques. This seminar will cover key methods such as K-Means, Hierarchical Clustering, Principal Component Analysis (PCA), and t-distributed Stochastic Neighbor Embedding (t-SNE). These techniques are essential for uncovering hidden patterns and structures in data without predefined labels, making them invaluable for various applications in businesses.
Target Audience: This seminar is designed for professionals and researchers from Small and Medium-sized Enterprises (SMEs) who are interested in leveraging data-driven insights to enhance decision-making processes. Whether you are a data analyst, business strategist, or technical lead, this seminar will provide you with the foundational knowledge and practical skills to apply unsupervised learning techniques in your domain.
Learning Objectives:
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Understand the fundamental concepts and algorithms of unsupervised learning.
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Explore various clustering techniques and their applications.
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Learn how to reduce data dimensionality while preserving essential information.
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Gain insights into the practical implementation of these techniques using Python.
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Evaluate and compare different unsupervised learning methods using performance metrics.
Prerequisites: Participants should have a basic understanding of data analysis and Python programming. Familiarity with machine learning concepts will be beneficial but is not mandatory.
Indicative Contents:
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Introduction to Clustering
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K-Means Clustering: Concept, Algorithm, and Python Implementation
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Hierarchical Clustering: Agglomerative and Divisive Approaches
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Comparison of Clustering Methods using Silhouette Score
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Dimensionality Reduction Techniques
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Principal Component Analysis (PCA): Concept, Mathematical Formulation, and Python Implementation
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t-Distributed Stochastic Neighbor Embedding (t-SNE): Concept, Algorithm, and Applications
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Practical Applications and Case Studies
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Hands-on Session: Implementing Clustering and Dimensionality Reduction in Python
Join us to enhance your understanding of unsupervised learning and discover how these techniques can transform your data into actionable insights.
The project is co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them.
