# Class meeting for 10-405 SGD and Hash Kernels

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.

### Slides

Stochastic gradient descent:

### Quiz

### 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
- 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