Difference between revisions of "10-601 K-NN And Trees - Lecture from Fall 2013"
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| − | This a lecture used in the [[Syllabus for Machine Learning 10-601]] | + | This a lecture used in the [[Syllabus for Machine Learning 10-601 in Fall 2013]] |
=== Slides === | === Slides === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-601/decision-trees.pptx Slides in Powerpoint]. | + | * [http://www.cs.cmu.edu/~wcohen/10-601/classification-and-decision-trees.pptx Slides in Powerpoint]. |
=== Readings === | === Readings === | ||
| Line 11: | Line 11: | ||
=== What You Should Know Afterward === | === 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 | ||
Latest revision as of 13:19, 15 August 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2013
Slides
Readings
- Mitchell, Chapter 3.
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
