Difference between revisions of "Class meeting for 10-405 Parallel Perceptrons"
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=== Readings === | === Readings === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-601/vp-notes/vp.pdf Notes on voted perceptron.] | + | * [http://www.cs.cmu.edu/~wcohen/10-601/vp-notes/vp.pdf Notes on voted perceptron.] Note: these were updated --[[User:Wcohen|Wcohen]] ([[User talk:Wcohen|talk]]) 10:28, 6 March 2018 (EST) |
=== Optional Readings === | === Optional Readings === | ||
Latest revision as of 11:28, 6 March 2018
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-405 in Spring 2018.
Slides
- Lecture 1: in Powerpoint, in PDF.
- Lecture 2: in Powerpoint, in PDF.
- Lecture 3: : in Powerpoint, in PDF.
Quiz
Readings
- Notes on voted perceptron. Note: these were updated --Wcohen (talk) 10:28, 6 March 2018 (EST)
Optional Readings
- Large Margin Classification Using the Perceptron Algorithm, Freund and Schapire, MLJ 1999
- Discriminative Training Methods for Hidden Markov Models, Collins EMNLP 2002.
- Distributed Training Strategies for the Structured Perceptron, R. McDonald, K. Hall and G. Mann, North American Association for Computational Linguistics (NAACL), 2010.
Things to Remember
- Definition of mistake bound
- Definition of perceptron algorithm
- Mistake bound analysis for perceptrons, in terms of margin and example radius
- Converting perceptrons to batch: voted perceptron, averaged perceptron
- Definition of the ranking perceptron and kernel perceptron
- Relationship of hash trick to kernels
- Parallellizing streaming ML algorithms
- Parameter mixing, and the effect it has on the mistake bounds for perceptrons
- Iterative parameter mixing, and the effect it has on the mistake bounds for perceptrons
- The ALLREDUCE algorithm and its complexity
