Difference between revisions of "10-601 Classification and K-NN"
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=== Slides === | === Slides === | ||
| − | * Ziv's lecture: | + | * 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]. | * William's lecture (draft): [http://www.cs.cmu.edu/~wcohen/10-601/classification-and-knn.pptx Slides in Powerpoint]. | ||
Revision as of 09:03, 3 September 2014
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 (draft): 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.
- How the value of K affects the tendency of K-NN to overfit or underfit data.
- (optional) probabilistic interpretation of KNN decisions
