Difference between revisions of "10-601 Decision Trees"
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| − | This a lecture used in the [[Syllabus for Machine Learning 10- | + | This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] |
=== Slides === | === Slides === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-601/decision-trees.pptx Slides in Powerpoint]. | + | * William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/decision-trees.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/decision-trees.pdf in PDF]. |
=== Readings === | === Readings === | ||
| − | * Mitchell, Chapter 3. | + | * Mitchell, Chapter 3 or Murphy 16.1-16.2 |
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
| − | * | + | * What a decision tree is, and how to classify an instance using a decision tree. |
| + | * What the canonical top-down algorithm is for learning a decision tree. | ||
| + | ** What heuristics are used for choosing a decision-tree split. | ||
| + | ** What entropy is, and what information gain is. | ||
| + | * What reduced-error pruning is, and why it might improve classification performance. | ||
| + | * Some of the advantages and disadvantages of decision-tree learning in specific, and eager learning in general, compared to K-NN learning. | ||
Latest revision as of 15:58, 6 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
- William's lecture: Slides in Powerpoint, in PDF.
Readings
- Mitchell, Chapter 3 or Murphy 16.1-16.2
What You Should Know Afterward
- What a decision tree is, and how to classify an instance using a decision tree.
- What the canonical top-down algorithm is for learning a decision tree.
- What heuristics are used for choosing a decision-tree split.
- What entropy is, and what information gain is.
- What reduced-error pruning is, and why it might improve classification performance.
- Some of the advantages and disadvantages of decision-tree learning in specific, and eager learning in general, compared to K-NN learning.
