Class meeting for 10-405 SGD and Hash Kernels
From Cohen Courses
Revision as of 12:32, 5 March 2018 by Wcohen
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
- For logistic regression, and the sparse updates for it: Lazy Sparse Stochastic Gradient Descent for Regularized Multinomial Logistic Regression, Carpenter, Bob. 2008. See also his blog post on logistic regression. I also recommend Charles Elkan's notes on logistic regression (local saved copy).
- For hash kernels: Feature Hashing for Large Scale Multitask Learning, Weinberger et al, ICML 2009.
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