10-601 Classification and K-NN

This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014

Slides

• Ziv's lecture: Slides in pdf.

• William's lecture: Slides in Powerpoint, in PDF.

Readings

• Mitchell, Chapters 1,2 and 8.

What You Should Know Afterward

• What is the goal of classification
• What sorts of problems can be solved by reducing them to classification
• What the K-NN algorithm is.
• How the value of K affects the tendency of K-NN to overfit or underfit data.

• Optional:
  • Bayes decision boundary for classification
  • Is there an optimal classifier?
  • What decision boundary is defined by K-NN.
  • Probabilistic interpretation of KNN decisions