# Class meeting for 10-405 Parallel Perceptrons

From Cohen Courses

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

- 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