Machine Learning Meetup Notes: 2010-04-21 - Revision history

Mschachter at 18:36, 22 April 2010

2010-04-22T18:36:48Z

← Older revision		Revision as of 11:36, 22 April 2010
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	=== 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

Mschachter at 18:36, 22 April 2010

2010-04-22T18:36:31Z

← Older revision		Revision as of 11:36, 22 April 2010
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	**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		*Writeups on Optimization
	**Gradient Descent/Conjugate Gradient: http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf		**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		**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		*Python Linear Least Squares Fitting Routine: http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html

Mschachter at 18:35, 22 April 2010

2010-04-22T18:35:15Z

← Older revision		Revision as of 11:35, 22 April 2010
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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 ===

Mschachter at 18:34, 22 April 2010

2010-04-22T18:34:15Z

← Older revision		Revision as of 11:34, 22 April 2010
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	**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
			*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		*Python Linear Least Squares Fitting Routine: http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html

Mschachter at 18:32, 22 April 2010

2010-04-22T18:32:42Z

← Older revision		Revision as of 11:32, 22 April 2010
Line 10:		Line 10:
	**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
			*Python Linear Least Squares Fitting Routine: http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html

Mschachter: Created page with '=== 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…'

2010-04-22T18:31:38Z

Created page with '=== 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…'

New page

=== 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

=== 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

Machine Learning Meetup Notes: 2010-04-21 - Revision history

Mschachter at 18:36, 22 April 2010

Mschachter at 18:36, 22 April 2010

Mschachter at 18:35, 22 April 2010

Mschachter at 18:34, 22 April 2010

Mschachter at 18:32, 22 April 2010

Mschachter: Created page with '=== 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…'

Mschachter: Created page with '=== 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…'