Section outline

  • ·         Overview: This lesson introduces the k-Nearest Neighbors (KNN) algorithm, a simple yet powerful method that makes predictions based on similarity to known examples. Students learn how “show me your neighbors, and I’ll tell you who you are” works in practice, including how to choose the number of neighbors and measure similarity.

    • Micro-Topic 8.1: Introduction to Nearest Neighbors – Goal: Understand the basic idea of using closest examples to make predictions.

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    • Micro-Topic 8.2: Measuring Similarity – Distance Metrics – Goal: Learn how to quantify “nearest” using distance measures.

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    • Micro-Topic 8.3: Choosing K (Number of Neighbors) – Goal: Understand how the choice of K affects predictions and the balance between noise and generalization.

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    • Micro-Topic 8.4: Applying KNN – A Simple Example – Goal: See KNN in action with a concrete, easy-to-grasp example.

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    • Micro-Topic 8.5: Pros and Cons of KNN – Goal: Summarize why KNN can be useful and where it struggles.

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