December 2016 - Revision history

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2017-04-17T02:40:02Z

Bot: Orphan page, add template

← Older revision		Revision as of 02:40, 17 April 2017
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			{{Orphan\|date=April 2017}}
	=Projects=		=Projects=

Admin: /* UW Machine Learning Courses */

2016-12-25T07:43:08Z

UW Machine Learning Courses

← Older revision		Revision as of 07:43, 25 December 2016
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	ML Regression: https://www.coursera.org/learn/ml-regression		ML Regression: https://www.coursera.org/learn/ml-regression

			ML Classificaiton: https://www.coursera.org/learn/ml-classification

			Predictive Analytics Models: https://www.coursera.org/learn/predictive-analytics

	==Scikit Learn==		==Scikit Learn==

Admin: /* References */

2016-12-25T07:41:12Z

References

← Older revision		Revision as of 07:41, 25 December 2016
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	=References=		=References=

			==UW Machine Learning Courses==

			ML Foundations: Case Study Approach https://www.coursera.org/learn/ml-foundations

			ML Regression: https://www.coursera.org/learn/ml-regression

	==Scikit Learn==		==Scikit Learn==

Admin: /* Scikit Learn */

2016-12-25T07:35:10Z

Scikit Learn

← Older revision		Revision as of 07:35, 25 December 2016
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	==Scikit Learn==		==Scikit Learn==

			===Examples===

	Scikit learn examples: http://scikit-learn.org/stable/auto_examples/index.html		Scikit learn examples: http://scikit-learn.org/stable/auto_examples/index.html
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	F regression: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html		F regression: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html

			Gaussian Process Model regression: http://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html

			===Datasets===

	Abalone data set (UCI ML repo): http://archive.ics.uci.edu/ml/datasets/abalone		Abalone data set (UCI ML repo): http://archive.ics.uci.edu/ml/datasets/abalone

			===References===

	Tutorial on support vector regression: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=C72B60C38C905614709A0C30D973E2CB?doi=10.1.1.114.4288&rep=rep1&type=pdf		Tutorial on support vector regression: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=C72B60C38C905614709A0C30D973E2CB?doi=10.1.1.114.4288&rep=rep1&type=pdf

Admin: /* Scikit Learn */

2016-12-25T07:31:51Z

Scikit Learn

← Older revision		Revision as of 07:31, 25 December 2016
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	Feature selection (picking out the high-variance variables): http://scikit-learn.org/stable/modules/feature_selection.html#l1-feature-selection		Feature selection (picking out the high-variance variables): http://scikit-learn.org/stable/modules/feature_selection.html#l1-feature-selection

			F regression: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html

	Abalone data set (UCI ML repo): http://archive.ics.uci.edu/ml/datasets/abalone		Abalone data set (UCI ML repo): http://archive.ics.uci.edu/ml/datasets/abalone

Admin: /* Scikit Learn */

2016-12-25T07:30:38Z

Scikit Learn

← Older revision		Revision as of 07:30, 25 December 2016
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	Scikit learn more about SVR: http://scikit-learn.org/stable/modules/svm.html		Scikit learn more about SVR: http://scikit-learn.org/stable/modules/svm.html

			Feature selection (picking out the high-variance variables): http://scikit-learn.org/stable/modules/feature_selection.html#l1-feature-selection

	Abalone data set (UCI ML repo): http://archive.ics.uci.edu/ml/datasets/abalone		Abalone data set (UCI ML repo): http://archive.ics.uci.edu/ml/datasets/abalone

Admin: /* References */

2016-12-25T07:28:47Z

References

← Older revision		Revision as of 07:28, 25 December 2016
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	=References=		=References=

			==Scikit Learn==

	Scikit learn examples: http://scikit-learn.org/stable/auto_examples/index.html		Scikit learn examples: http://scikit-learn.org/stable/auto_examples/index.html
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	Tutorial on support vector regression: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=C72B60C38C905614709A0C30D973E2CB?doi=10.1.1.114.4288&rep=rep1&type=pdf		Tutorial on support vector regression: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=C72B60C38C905614709A0C30D973E2CB?doi=10.1.1.114.4288&rep=rep1&type=pdf

	~~Wiley Classics library~~: https://www.~~librarything~~.~~com~~/~~series~~/~~Wiley+Classics+Library~~		==Bioinformatics==

			Bioinformatics course from David Page: https://www.biostat.wisc.edu/bmi576/

			David Page: http://pages.cs.wisc.edu/~dpage/

			''E. coli'' genome project: https://www.genome.wisc.edu/

			==Machine Learning==

	Tombone's computer vision blog: http://www.computervisionblog.com/		Tombone's computer vision blog: http://www.computervisionblog.com/

	Extended mean field restricted boltzmann machine: https://github.com/charlesmartin14/emf-rbm		Extended mean field restricted boltzmann machine: https://github.com/charlesmartin14/emf-rbm

			==Books==

			Wiley Classics library: https://www.librarything.com/series/Wiley+Classics+Library

Admin at 07:03, 25 December 2016

2016-12-25T07:03:08Z

@@ Line 1: / Line 1: @@
 Projects for the month of December:
 * Empirical model building - Github repository containing Jupyter notebooks illustrating various experimental design strategies
 ** Website: https://charlesreid1.github.io/empirical-model-building
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 ** Kaggle Notebook: Cleaning Up the Crime Scene: Parsing the Data https://www.kaggle.com/csc142/d/fbi-us/california-crime/cleaning-up-the-crime-scene-parsing-the-data
 ** Kaggle Notebook: California Crime Compendium: Comparing Campuses https://www.kaggle.com/csc142/d/fbi-us/california-crime/california-crime-compendium-comparing-campuses

Admin: Created page with "Projects for the month of December: * Empirical model building - Github repository containing Jupyter notebooks illustrating various experimental design strategies ** Website:..."

2016-12-25T06:49:08Z

Created page with "Projects for the month of December: * Empirical model building - Github repository containing Jupyter notebooks illustrating various experimental design strategies ** Website:..."

New page

Projects for the month of December:
* Empirical model building - Github repository containing Jupyter notebooks illustrating various experimental design strategies
** Website: https://charlesreid1.github.io/empirical-model-building
** Repository: https://github.com/charlesreid1/empirical-model-building

* Rejoyce - Github repository containing Jupyter notebooks applying the Natural Language Toolkit to James Joyce's Ulysses
** Website: https://charlesreid1.github.io/rejoyce
** Repository: https://github.com/charlesreid1/rejoyce

* California Crime Stats - Github repository containing Jupyter notebooks applying various statistical tools to analyzing California crime data
** Repository: https://github.com/charlesreid1/cali-crime
** Kaggle Notebook: Cleaning Up the Crime Scene: Parsing the Data https://www.kaggle.com/csc142/d/fbi-us/california-crime/cleaning-up-the-crime-scene-parsing-the-data
** Kaggle Notebook: California Crime Compendium: Comparing Campuses https://www.kaggle.com/csc142/d/fbi-us/california-crime/california-crime-compendium-comparing-campuses