# Machine Learning Meetup Notes: 2010-04-21

From Noisebridge

### [edit] 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

### [edit] Details

- Some good books and references on linear regression/machine learning:
- 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
- Good ML Tutorials: http://www.autonlab.org/tutorials/

- 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