Class meeting for 10-405 SGD and Hash Kernels

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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.


Stochastic gradient descent:


Readings for the Class

Optional readings

Things to Remember

  • Approach of learning by optimization
  • Optimization goal for logistic regression
  • Key terms: logistic function, sigmoid function, log conditional likelihood, loss function, stochastic gradient descent
  • Updates for logistic regression, with and without regularization
  • The formal properties of sparse logistic regression
    • Whether it is exact or approximate
    • How it changes memory and time usage
  • Formalization of logistic regression as matching expectations between data and model
  • Regularization and how it interacts with overfitting
  • How "sparsifying" regularization affects run-time and memory
  • What the "hash trick" is and why it should work