10-601 K-NN And Trees - Lecture from Fall 2013

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This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2013



  • Mitchell, Chapter 3.

What You Should Know Afterward

  • What is the goal of classification
  • Bayes decision boundary for classification
  • Is there an optimal classifier?
  • What the K-NN algorithm is.
  • What the computational properties of eager vs lazy learning are in general, and K-NN in specific.
  • What decision boundary is defined by K-NN, and how it compares to decision boundaries of linear classifiers.
  • How the value of K affects the tendency of K-NN to overfit or underfit data.
  • (optional) probabilistic interpretation of KNN decisions