Difference between revisions of "Class meeting for 10-605 Parallel Perceptrons 1"

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This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2014|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2014]].
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This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2015|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2015]].
  
 
=== Slides ===
 
=== Slides ===
  
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* [http://www.cs.cmu.edu/~wcohen/10-605/mistake-bounds.pptx Slides in Powerpoint]
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Catchup from Tuesday:
  
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=== Readings for the Class ===
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* [http://www.cs.cmu.edu/~wcohen/10-605/2016/sgd-part2.pptx Slides in Powerpoint]
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*[http://www.ryanmcd.com/papers/parallel_perceptronNAACL2010.pdf Distributed Training Strategies for the Structured Perceptron], R. McDonald, K. Hall and G. Mann, North American Association for Computational Linguistics (NAACL), 2010.
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* [http://www.cs.cmu.edu/~wcohen/10-605/2016/sgd-part2.pdf Slides in PDF]
  
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=== Optional Readings ===
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Perceptrons:
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* [http://www.cs.cmu.edu/~wcohen/10-707/vp-notes/vp.pdf Notes on voted perceptron.]
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* [http://www.cs.cmu.edu/~wcohen/10-605/2016/mistake-bounds+struct-vp-1.pptx Slides in Powerpoint]
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* [http://www.cs.cmu.edu/~wcohen/10-605/2016/mistake-bounds+struct-vp-1.pdf Slides in PDF]
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=== 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].
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* Optional reading:  Freund, Yoav, and Robert E. Schapire. "Large margin classification using the perceptron algorithm." Machine learning 37.3 (1999): 277-296.
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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]
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=== What You Should Remember ===
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* The perceptron algorithm, and its complexity
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* Definitions: mistake, mistake bound, margin

Latest revision as of 16:40, 1 August 2017

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

  • Optional reading: Freund, Yoav, and Robert E. Schapire. "Large margin classification using the perceptron algorithm." Machine learning 37.3 (1999): 277-296.

What You Should Remember

  • The perceptron algorithm, and its complexity
  • Definitions: mistake, mistake bound, margin
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