[RCAC Workshop]ML Fundamentals II: Unsupervised Learning

📅 Date: October 9th, 2026 ⏰ Time: 2PM-3PM 💻 Location: Virtual 🏫 Instructor: Haniye Kashgarani

Who Should Attend Researchers, engineers, and students with basic programming or data science experience who want to expand their understanding of machine learning. This session is ideal for those working with real-world datasets who are interested in discovering hidden structures, patterns, or groupings in data without relying on labels.

What You’ll Learn This training introduces the fundamentals of unsupervised learning, where algorithms work with unlabeled data to uncover patterns and insights. You’ll learn about core methods such as clustering (e.g., k-means, hierarchical), and dimensionality reduction (e.g., PCA). The session will also cover practical applications like feature extraction, and data exploration. Key concepts such as choosing the right number of clusters, interpreting low-dimensional embeddings, and understanding limitations of unsupervised methods will be explained practically.

Level Beginner to intermediate

🔗 Register now: LINK