Class meeting for 10-405 Parallel Perceptrons

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This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-405 in Spring 2018.

Contents

Slides

Quiz

Readings

Optional Readings

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