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 ===
*Some good books on linear regression:
*Some good books and references on linear regression/machine learning:
**Excellent ebook: http://www-stat.stanford.edu/~tibs/ElemStatLearn/
**Excellent ebook: http://www-stat.stanford.edu/~tibs/ElemStatLearn/
**Classic ML Book: http://www.amazon.com/Pattern-Classification-2nd-Richard-Duda/dp/0471056693
**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
**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

Latest revision as of 11:36, 22 April 2010

Overview[edit]

  • 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[edit]