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This a lecture used in the [[Syllabus for Machine Learning 10-601 in Fall 2014]] === Slides === * Ziv's lecture: [http://www.cs.cmu.edu/~zivbj/classF14/classfication.pdf Slides in pdf]. * William's lecture (draft): [http://www.cs.cmu.edu/~wcohen/10-601/classification-and-knn.pptx Slides in Powerpoint]. === Readings === * Mitchell, Chapters 1,2 and 8. === 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.'' ** ''Ziv - shouldn't we move these till after we've introduced an eager learner and a linear classifier?'' --[[User:Wcohen|Wcohen]] ([[User talk:Wcohen|talk]]) 13:35, 15 August 2014 (EDT) * How the value of K affects the tendency of K-NN to overfit or underfit data. * (optional) probabilistic interpretation of KNN decisions

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