Difference between revisions of "10-601 Classification and K-NN"

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* Ziv's lecture: [http://www.cs.cmu.edu/~zivbj/classF14/classification.pdf Slides in pdf].
 
* Ziv's lecture: [http://www.cs.cmu.edu/~zivbj/classF14/classification.pdf Slides in pdf].
  
* William's lecture (draft): [http://www.cs.cmu.edu/~wcohen/10-601/classification-and-knn.pptx Slides in Powerpoint].
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* William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/classification-and-knn.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/classification-and-knn.pdf in PDF].
  
 
=== Readings ===
 
=== Readings ===

Latest revision as of 14:44, 17 September 2014

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

Slides

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