Difference between revisions of "Class meeting for 10-605 SGD and Hash Kernels"
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* For logistic regression, and the sparse updates for it: [http://lingpipe.files.wordpress.com/2008/04/lazysgdregression.pdf Lazy Sparse Stochastic Gradient Descent for Regularized Multinomial Logistic Regression], Carpenter, Bob. 2008. See also [http://alias-i.com/lingpipe/demos/tutorial/logistic-regression/read-me.html his blog post] on logistic regression. I also recommend [http://www.cs.cmu.edu/~wcohen/10-605/notes/elkan-logreg.pdf Charles Elkan's notes on logistic regression] (local saved copy). | * For logistic regression, and the sparse updates for it: [http://lingpipe.files.wordpress.com/2008/04/lazysgdregression.pdf Lazy Sparse Stochastic Gradient Descent for Regularized Multinomial Logistic Regression], Carpenter, Bob. 2008. See also [http://alias-i.com/lingpipe/demos/tutorial/logistic-regression/read-me.html his blog post] on logistic regression. I also recommend [http://www.cs.cmu.edu/~wcohen/10-605/notes/elkan-logreg.pdf Charles Elkan's notes on logistic regression] (local saved copy). | ||
* For hash kernels: [http://arxiv.org/pdf/0902.2206.pdf Feature Hashing for Large Scale Multitask Learning], Weinberger et al, ICML 2009. | * For hash kernels: [http://arxiv.org/pdf/0902.2206.pdf 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 | ||
| + | * 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 | ||
Revision as of 10:08, 16 October 2015
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall 2015.
Slides
Stochastic gradient descent:
Today's Quiz
https://qna-app.appspot.com/view.html?aglzfnFuYS1hcHByGQsSDFF1ZXN0aW9uTGlzdBiAgICg7MHcCgw
Readings for the Class
Optional readings
- 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
- 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
