Week 3 - k-Nearest Neighbors

Learning Objectives

After today's class, you should be able to:

• Understand the k-Nearest Neighbor (kNN) algorithm for classification tasks, enabling the extraction of insights by analyzing patterns in labeled data.
• Apply the k-Nearest Neighbors (k-NN) algorithm manually to a small dataset, including scaling the data, to understand how distance calculations determine the predicted outcome.

Class Agenda

• k-Nearest Neighbors (k-NN) Slides
• [5 min] Break (Optional)
• In Class Time for CP2_k-Nearest_Neighbors

Task List

• Required Reading: Chapter 7 - k-Nearest Neighbors (k-NN)
• Complete and Submit CP2_k-Nearest_Neighbors on Canvas.

Additional Resources

• Chapter 7 Code Examples
• CP2_k-Nearest_Neighbors_Worksheet