Section outline

  • ·         Overview: This lesson explores unsupervised learning (finding patterns without labels) through clustering, and then introduces semi-supervised learning, which bridges supervised and unsupervised methods. Students will see how algorithms like K-Means form clusters, why choosing the number of clusters is tricky, and how unlabeled data combined with a bit of labeled data can improve learning.

    • Micro-Topic 11.1: Unsupervised Learning & Clustering Basics – Goal: Explain what unsupervised learning is and introduce clustering as grouping data by similarity.

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    • Micro-Topic 11.2: K-Means Clustering Algorithm – Goal: Teach how the K-Means algorithm partitions data into a chosen number of clusters.

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    • Micro-Topic 11.3: Choosing K and Cluster Evaluation – Goal: Discuss the challenge of picking the right number of clusters and how to evaluate clustering quality.

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    • Micro-Topic 11.4: Applications of Clustering – Goal: Provide concrete examples of how clustering is used in practice.

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    • Micro-Topic 11.5: Introduction to Semi-Supervised Learning – Goal: Define semi-supervised learning and explain why it’s useful.

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    • Micro-Topic 11.6: Semi-Supervised Techniques – Pseudo-Labeling & Label Propagation – Goal: Outline common approaches to perform semi-supervised learning.

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