Difference between revisions of "Class meeting for 10-605 Parallel Perceptrons 1"
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=== Preparation for the Class === | === Preparation for the Class === | ||
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* Read [http://www.cs.cmu.edu/~wcohen/10-601/vp-notes/vp.pdf my notes on the voted perceptron]. Alternatively, or in addition, you can '''view the lecture''' for 10-601 from 9/22/14 or 9/23/14, which can be accessed [https://mediatech-stream.andrew.cmu.edu/Mediasite/Catalog/Full/4e86c44694a14b9fbe1ea7653f553ac621 via MediaTech]. | * Read [http://www.cs.cmu.edu/~wcohen/10-601/vp-notes/vp.pdf my notes on the voted perceptron]. Alternatively, or in addition, you can '''view the lecture''' for 10-601 from 9/22/14 or 9/23/14, which can be accessed [https://mediatech-stream.andrew.cmu.edu/Mediasite/Catalog/Full/4e86c44694a14b9fbe1ea7653f553ac621 via MediaTech]. | ||
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* Optional background on linear algebra, if you need it: [http://www.cs.cmu.edu/~zkolter/course/linalg/ Zico Kolter's linear algebra review lectures] | * Optional background on linear algebra, if you need it: [http://www.cs.cmu.edu/~zkolter/course/linalg/ Zico Kolter's linear algebra review lectures] | ||
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| + | === What You Should Remember === | ||
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| + | * The perceptron algorithm, and its complexity | ||
| + | * Definitions: mistake, mistake bound, margin | ||
| + | * The dual form of the perceptron, as a kernel method | ||
| + | * How the "hash trick" can be formalized as a kernel | ||
Revision as of 10:12, 16 October 2015
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Spring_2015.
Slides
Catchup from Tuesday:
Perceptrons:
Preparation for the Class
- Read my notes on the voted perceptron. Alternatively, or in addition, you can view the lecture for 10-601 from 9/22/14 or 9/23/14, which can be accessed via MediaTech.
- Optional reading: Freund, Yoav, and Robert E. Schapire. "Large margin classification using the perceptron algorithm." Machine learning 37.3 (1999): 277-296.
- Optional background on linear algebra, if you need it: Zico Kolter's linear algebra review lectures
What You Should Remember
- The perceptron algorithm, and its complexity
- Definitions: mistake, mistake bound, margin
- The dual form of the perceptron, as a kernel method
- How the "hash trick" can be formalized as a kernel
