Machine Learning Meetup Notes: 2010-04-21: Difference between revisions
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*Talked about gradient descent | *Talked about gradient descent | ||
*Passed around some python code for doing least squares | *Passed around some python code for doing least squares | ||
*Talked about starting a linear algebra mini-course | |||
*Talked about presenting stuff on SVMs at next meetup | |||
=== Details === | === Details === | ||
Revision as of 11:35, 22 April 2010
Overview
- Mike S talked about linear regression.
- Overview of linear least squares
- Talked about gradient descent
- Passed around some python code for doing least squares
- Talked about starting a linear algebra mini-course
- Talked about presenting stuff on SVMs at next meetup
Details
- Some good books on linear regression:
- Excellent ebook: http://www-stat.stanford.edu/~tibs/ElemStatLearn/
- Classic ML Book: http://www.amazon.com/Pattern-Classification-2nd-Richard-Duda/dp/0471056693
- Another ML Book (passed around in meetup): http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
- Writeups on Optimization
- Gradient Descent/Conjugate Gradient: http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf
- Least Angle Regression: http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf
- Python Linear Least Squares Fitting Routine: http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html