Difference between revisions of "10-601 Classification and K-NN"
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* Is there an optimal classifier? | * Is there an optimal classifier? | ||
* What the K-NN algorithm is. | * What the K-NN algorithm is. | ||
| − | * What the computational properties of eager vs lazy learning are in general, and K-NN in specific. | + | * ''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. | + | * ''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. | * How the value of K affects the tendency of K-NN to overfit or underfit data. | ||
* (optional) probabilistic interpretation of KNN decisions | * (optional) probabilistic interpretation of KNN decisions | ||
Revision as of 12:35, 15 August 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
- Ziv's lecture: to be posted
- 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
