Machine Learning & Data Mining

Two qualifications apply to the valid use of a t-test.

1) the distributions of the populations from which the samples are drawn can be assumed to be normal.

If this assumption is not valid, then a distribution-free or nonparametrictest such as the Wilcoxon (also called Mann-Whitney) should be used.

2) Less importantly, the t-test strictly defined assumes that the variances of the populations are equal.

If this assumption is not made, a variant called Welch’s t-test is used.

Type I error, or a "false positive": the error of rejecting a null hypothesis when it is actually true:

poor specificity

Type II error, or a "false negative": the error of failing to reject a null hypothesis when it is in not true:

poor sensitivity

The P value is a probability, with a value ranging from zero to one. If the P value is small, you'll conclude that the difference between sample means is unlikely to be a coincidence. Instead, you'll conclude that the populations have different means.

What is a null hypothesis?

When statisticians discuss P values, they use the term null hypothesis. The null hypothesis simply states that there is no difference between the groups. Using that term, you can define the P value to be the probability of observing a difference as large or larger than you observed if the null hypothesis were true.

Common misinterpretation of a P value

Many people misunderstand P values. If the P value is 0.03, that means that there is a 3% chance of observing a difference as large as you observed even if the two population means are identical (the null hypothesis is true). It is tempting to conclude, therefore, that there is a 97% chance that the difference you observed reflects a real difference between populations and a 3% chance that the difference is due to chance. However, this would be an incorrect conclusion. What you can say is that random sampling from identical populations would lead to a difference smaller than you observed in 97% of experiments and larger than you observed in 3% of experiments. This distinction may be more clear after you read A Bayesian perspective.

https://habrahabr.ru/post/276369/ read links inside

https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal

https://www.reddit.com/r/Python/comments/4j0xhf/indepth_machine_learning_course_w_python_xpost/

https://www.datacamp.com/courses/kaggle-python-tutorial-on-machine-learning

https://www.youtube.com/watch?v=G4V8owAOqrY&feature=youtu.be

https://news.ycombinator.com/item?id=11555551 time series

https://www.reddit.com/r/programming/comments/4eyyhm/google_has_started_a_new_video_series_teaching/

https://www.youtube.com/watch?v=cKxRvEZd3Mw

http://programmingzen.com/2016/04/19/big-data-university-educating-one-million-data-scientists/

https://studywolf.wordpress.com/2012/11/25/reinforcement-learning-q-learning-and-exploration/

https://habrahabr.ru/post/280766/

https://habrahabr.ru/company/spbifmo/blog/276479/

https://habrahabr.ru/company/spbifmo/blog/277511/

https://habrahabr.ru/company/spbifmo/blog/277593/

https://habrahabr.ru/company/spbifmo/blog/278069/

https://habrahabr.ru/post/276355/

https://news.ycombinator.com/item?id=10951457

https://medium.com/@daylenyang/building-chess-id-99afa57326cd#.8nx0aybxs

https://blog.skcript.com/machine-learning-in-a-week-a0da25d59850#.gdf8iyud2

https://medium.com/innoarchitech-innovation-architecture-technology/machine-learning-an-in-depth-non-technical-guide-part-5-b134da025a3e#.przhq3gu6

https://github.com/okulbilisim/awesome-datascience

https://github.com/josephmisiti/awesome-machine-learning

https://github.com/ujjwalkarn/Machine-Learning-Tutorials

https://medium.com/innoarchitech-innovation-architecture-technology/machine-learning-an-in-depth-non-technical-guide-part-3-b7a5d3911d3b#.7nzte37ek

https://news.ycombinator.com/item?id=10951276

https://www.udacity.com/course/deep-learning--ud730

https://habrahabr.ru/post/279665/ simpson paradox pandas

Markov Chain

http://setosa.io/ev/markov-chains/

https://news.ycombinator.com/item?id=11323122

https://habrahabr.ru/company/wunderfund/blog/279545/ MCMC pandas

http://www.inference.vc/deep-learning-is-easy/

https://docs.google.com/presentation/d/1O6ozzZHHxGzU-McpvEG09hl7K6oQDd2Taw0FOlnxJc8/preview?slide=id.p

Classes

https://www.coursera.org/specializations/big-data

https://www.dataquest.io/subscribe

https://habrahabr.ru/company/bitrix/blog/275455/

Online classes

https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/

https://work.caltech.edu/telecourse.html

https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about

https://github.com/donnemartin/data-science-ipython-notebooks

http://blog.cambridgecoding.com/2016/01/03/getting-started-with-regression-and-decision-trees/

Java

https://github.com/h2oai/h2o-3 H2O

http://blog.datumbox.com/datumbox-machine-learning-framework-0-6-1-released/

https://www.youtube.com/watch?v=s-i6nzXQF3g Flask + Machine learning

https://peadarcoyle.wordpress.com/2015/12/23/three-things-i-wish-i-knew-earlier-about-machine-learning/

http://www.john-foreman.com/blog/surviving-data-science-at-the-speed-of-hype

https://jakevdp.github.io/blog/2015/08/07/frequentism-and-bayesianism-5-model-selection/

http://grigory.us/blog/modern-intro-algorithms/

https://github.com/ianozsvald/data_science_delivered

https://iwringer.wordpress.com/2015/10/06/techniques-for-learning-from-large-amounts-of-data/

https://github.com/pbharrin/machinelearninginaction

http://blog.sigopt.com/post/134931842613/sigopt-fundamentals-likelihood-for-gaussian

http://blog.cloudera.com/blog/2015/12/common-probability-distributions-the-data-scientists-crib-sheet/

https://news.ycombinator.com/item?id=10723911

http://habrahabr.ru/post/273363/ XGBoost

https://github.com/dmlc/xgboost XGBoost

https://dato.com/products/create/

Линейная регрессия прогнозирует цену дома, как линейную комбинацию ширины и длины. Но цена дома в первую очередь зависит от площади дома, которая никак не выражается через линейную комбинацию длины и ширины. Поэтому, качество алгоритма существенным образом увеличивается, если длину и ширину заметить на их произведение.

Feature Selection

http://techblog.appnexus.com/blog/2016/01/04/ad-viewability-and-feature-selection-for-big-data/

https://habrahabr.ru/company/aligntechnology/blog/303750/

http://habrahabr.ru/post/264915/ feature selection

https://www.youtube.com/watch?v=BW3paX2g3dI&feature=share feature selection

http://habrahabr.ru/post/264241/

http://habrahabr.ru/post/264139/

Feature extraction and feature selection involve a balanced combination of domain expertise,

intuition, and mathematical methods. Feature extraction and dimension reduction can be combined in one step using principal component analysis (PCA), linear discriminant analysis (LDA), or canonical correlation analysis (CCA) techniques as a pre-processing step, followed by clustering by k-NN on feature vectors in reduced-dimension space. In machine learning this process is also called low-dimensional embedding.^[13]

SVD

http://www.ams.org/samplings/feature-column/fcarc-svd

http://danluu.com/linear-hammer/

https://habrahabr.ru/post/275273/ SVD dimention reduction

https://www.youtube.com/watch?v=R9UoFyqJca8

https://www.reddit.com/r/MachineLearning/comments/44qb9p/python_implementation_of_boruta_an_all_relevant/

https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient

Linear models

http://www.slideshare.net/BradKlingenberg/linear-models-for-data-science

https://habrahabr.ru/post/278513/

https://habrahabr.ru/post/279117/

http://www.datasciencecentral.com/group/resources/forum/topics/10-types-of-regressions-which-one-to-use

http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/copy.html Book

https://edumine.wordpress.com/2013/09/12/reading-list-on-algorithms-machine-learning-text-mining-statistics-java-and-mathematics/

http://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/

http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

http://www.slideshare.net/xamat/10-more-lessons-learned-from-building-machine-learning-systems

http://ciml.info/

http://work.caltech.edu/telecourse.html

http://www.cs.huji.ac.il/~shais/IML2014.html

Anomaly Detection

https://iwringer.wordpress.com/2015/11/17/anomaly-detection-concepts-and-techniques/

https://www.mapr.com/blog/better-anomaly-detection-t-digest-whiteboard-walkthrough

https://www.mapr.com/blog/anomaly-detection-poisson-distribution-whiteboard-walkthrough

https://www.datascience.com/blog/intro-to-anomaly-detection-learn-data-science-tutorials

https://medium.com/@iliasfl/data-science-tricks-simple-anomaly-detection-for-metrics-with-a-weekly-pattern-2e236970d77#.43mcl6cv5

http://kldavenport.com/

http://multithreaded.stitchfix.com/blog/2015/05/26/significant-sample/

https://github.com/haifengl/smile Java Library for ML

http://www.analyticsvidhya.com/blog/2015/07/github-special-data-scientists-to-follow-best-tutorials/

http://www.math.uah.edu/stat/

http://blog.brakmic.com/data-science-for-losers-part-4-machine-learning/

http://xyclade.github.io/MachineLearning/

https://news.ycombinator.com/item?id=10190740

https://www.edx.org/course/scalable-machine-learning-uc-berkeleyx-cs190-1x

http://www.analyticsvidhya.com/blog/2015/08/common-machine-learning-algorithms

http://habrahabr.ru/company/mlclass/blog/266727/

http://www.itshared.org/2015/10/data-science-interview-questions.htm

https://jakevdp.github.io/blog/2015/10/17/analyzing-pronto-cycleshare-data-with-python-and-pandas/

Logistic regression

http://habrahabr.ru/company/io/blog/265007/

http://gormanalysis.com/logistic-regression-fundamentals/

Data exploration tool written in Java

http://fathom.info/mirador/

http://www.knime.org/

http://elki.dbs.ifi.lmu.de/

with an emphasis on unsupervised methods in cluster analysis and outlier detection.

https://github.com/rushter/data-science-blogs

http://www.computervisiontalks.com/

http://www.computervisiontalks.com/?s=Smola

http://alex.smola.org/teaching/cmu2013-10-701/

https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects

http://www.analyticsvidhya.com/blog/2015/07/dimension-reduction-methods/

http://habrahabr.ru/company/yandex/blog/261245/ BigData

http://habrahabr.ru/company/itinvest/blog/262155/

http://www.edupristine.com/blog/understanding-and-creating-decision-tree

https://dato.com/

BOOKS

http://www-bcf.usc.edu/~gareth/ISL/

http://statweb.stanford.edu/~tibs/ElemStatLearn/

http://greenteapress.com/thinkstats2/index.html

• Supervised Learning:

» kNN (k Nearest Neighbors)

» Naive Bayes

» Logistic Regression

» Support Vector Machines

» Random Forests

• Unsupervised Learning:

» Clustering

» Factor Analysis

» Latent Dirichlet Allocation

http://scholar-vit.livejournal.com/419467.html

http://scholar-vit.livejournal.com/419610.html

"У мистера Брауна два ребенка, хотя бы один из них мальчик. Какова вероятность, что оба ребенка - мальчики".

М-М М-Д Д-М Д-Д => Ответ 1/3

"У мистера Грина два ребенка. Один из них - мальчик, родившийся в среду. Какова вероятность того, что оба ребенка - мальчики?" Ответ => 13/27

https://probmods.org/ model of cognition

https://www.youtube.com/watch?v=T1O3ikmTEdA&feature=youtu.be&t=1244 Norvig

http://www.amplifypartners.com/interviews/on-the-evolution-of-machine-learning-from-linear-models-to-neural-networks/

http://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/

https://news.ycombinator.com/item?id=9562379

https://www.otexts.org/fpp/6/3 forecasting book

http://www.cs.cornell.edu/jeh/book11April2014.pdf

t-SNE

https://beta.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm

http://habrahabr.ru/post/267041/

http://quantombone.blogspot.com/2015/03/deep-learning-vs-machine-learning-vs.html

http://quantombone.blogspot.com/2015/04/deep-learning-vs-probabilistic.html

http://www.autonlab.org/tutorials/list.html

Signal Processing

http://www-math.mit.edu/~gs/papers/newsigproc.pdf

http://openrefine.org/

http://habrahabr.ru/post/252743/

http://zhengrui.github.io/zerryland/ML-CV-Resource.html

http://www.slideshare.net/dtunkelang/how-to-interview-a-data-scientist

https://github.com/rasbt/pattern_classification

http://sebastianraschka.com/

http://tempr.org/54f9f429588f8.html

http://www.automaticstatistician.com/

http://radimrehurek.com/data_science_python/

https://docs.google.com/document/d/1YN6BVdReNAYc8B0fjQ84yzDflqmeEPj7S0Xc-9_26R0/edit

Book

Machine Learning in Python: Essential Techniques for Predictive Analysis by Michael Bowles

ML CheatSheet

https://github.com/soulmachine/machine-learning-cheat-sheet

https://dineshramitc.wordpress.com/2014/12/05/ebook-a-course-in-machine-learning/

https://dineshramitc.wordpress.com/2014/12/05/ebook-introduction-to-machine-learning/

http://www.win-vector.com/blog/2014/12/the-geometry-of-classifiers/

http://www.startup.ml/resources

http://habrahabr.ru/post/247751/ python machine learning

http://www.nature.com/doifinder/10.1038/nature14236

https://news.ycombinator.com/item?id=9130852

Classes

http://igorsubbotin.blogspot.ru/p/data-science.html

http://evelinag.com/blog/2014/12-15-christmas-carol-and-other-eigenvectors/index.html#.VI_HIyvF_y9

Mining Massive Data Sets

https://vk.com/russianpopup

http://zaleslaw.blogspot.ru/

http://mmds.org/

http://www-labs.iro.umontreal.ca/~bengioy/dlbook/ BOOK

http://arxiv.org/abs/1412.0291 Bits from Biology for Computational Intelligence

https://news.ycombinator.com/item?id=9131991

http://blogs.technet.com/b/machinelearning/

http://neuralnetworksanddeeplearning.com/ BOOK online

http://www.cognitivealgorithm.info/

Smile (Java)

https://github.com/haifengl/smile

https://news.ycombinator.com/item?id=9131991

https://haifengl.wordpress.com/2014/11/20/smile-is-available-on-github/

http://www.cs.uvm.edu/~icdm/algorithms/10Algorithms-08.pdf

http://www.datasciencecentral.com/profiles/blogs/great-list-of-resources-data-science-visualization-machine

http://www.machinalis.com/blog/support-vector-machines/

https://colah.github.io/posts/2014-10-Visualizing-MNIST/

http://www.cs.mcgill.ca/~sqrt/dimr/dimreduction.html

http://alexey.radul.name/ideas/2015/how-to-compute-with-a-probability-distribution/

http://blog.explainmydata.com/

https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/

http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/

http://bid2.berkeley.edu/bid-data-project/

https://www.youtube.com/watch?v=xeAB10QgDW8

http://numericinsight.blogspot.com/2014/07/a-gentle-introduction-to-backpropagation.html

https://news.ycombinator.com/item?id=8135890

Book: Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data By Byron Ellis

http://web.bryant.edu/~bblais/statistical-inference-for-everyone-sie.html

https://github.com/josephmisiti/awesome-machine-learning

http://hunch.net/

http://www.metacademy.org/

http://datasciencemasters.org/

http://metacademy.org/roadmaps/cjrd/level-up-your-ml

https://news.ycombinator.com/item?id=8061628

http://arxiv.org/abs/1407.5019 NN

https://probmods.org/ cognition

http://fastml.com/

http://www.zipfianacademy.com/

https://news.ycombinator.com/item?id=8120053

http://www.technologyreview.com/news/529691/ibm-chip-processes-data-similar-to-the-way-your-brain-does/

PCA LCA

http://sebastianraschka.com/Articles/2014_python_lda.html

https://habrahabr.ru/post/304214/ PCA explained

http://notmatthancock.github.io/2015/06/14/what-is-pca.html

http://setosa.io/ev/principal-component-analysis/

https://news.ycombinator.com/item?id=9040266

http://liorpachter.wordpress.com/2014/05/26/what-is-principal-component-analysis/

http://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/ Principal Components Analysis

http://ganeshiyer.net/blog/2013/10/22/machine-learning-with-javascript/

http://habrahabr.ru/post/241527/

ML books

http://www.cs.ubc.ca/~murphyk/MLbook/

https://dl.dropboxusercontent.com/u/31779972/DataMiningForTheMasses.pdf

http://avaxhm.com/ebooks/programming_development/1783284358Clojure.html

http://christonard.com/12-free-data-mining-books/

https://en.wikipedia.org/wiki/Rprop

Clojure for Machine Learning book

Building Machine Learning Systems with Python book

Building Probabilistic Graphical Models with Python book

http://stackoverflow.com/questions/598726/overwhelmed-by-machine-learning-is-there-an-ml101-book?rq=1

Random Forest http://orbi.ulg.ac.be/handle/2268/170309

http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/

http://amitranga.wordpress.com/2014/06/08/feature-selection/

http://twiecki.github.io/bayesian_pymc3_europy_ab.slides.html#/

http://habrahabr.ru/post/238431/

http://habrahabr.ru/post/227293/

http://habrahabr.ru/post/225589/

http://habrahabr.ru/post/226641/

http://habrahabr.ru/company/surfingbird/blog/226677/ Sampling

http://habrahabr.ru/company/surfingbird/blog/228249/

http://habrahabr.ru/company/surfingbird/blog/230103/

http://machinelearningmastery.com/

Fourier for data analysis

http://see.stanford.edu/see/courseInfo.aspx?coll=84d174c2-d74f-493d-92ae-c3f45c0ee091

GENERIC AI

https://dl.dropboxusercontent.com/u/280585369/2013-clark.pdf Prediction Machine

http://gigaom.com/2014/05/23/meet-the-algorithm-that-can-learn-everything-about-anything/

https://news.ycombinator.com/item?id=7795621

http://lispm.de/genera-concepts

http://radar.oreilly.com/tag/intelligence-matters

http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms

https://news.ycombinator.com/item?id=7783550

Quantum computer

http://readwrite.com/2014/05/22/google-quantum-computer-project#awesm=~oF6NzB0ex2JLpE

http://christonard.com/12-free-data-mining-books/

http://www-bcf.usc.edu/~gareth/ISL/ BOOK

http://www.inf.ed.ac.uk/teaching/courses/iaml/

http://www.inf.ed.ac.uk/teaching/courses/mlpr/

http://www.inf.ed.ac.uk/teaching/courses/pmr/

http://machine-learning-course.joachims.org/

BAYES

https://news.ycombinator.com/item?id=10843680

http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html Bayes

http://habrahabr.ru/post/232639/ Bayes

http://blog.claymcleod.io/2016/02/02/Bayes-Theorem-for-Computer-Scientists/

http://habrahabr.ru/company/yandex/blog/208034/ Yandex lectures

http://efytimes.com/e1/fullnews.asp?edid=121516 free ML books

https://news.ycombinator.com/item?id=7120391 free ML books

http://www.visiondummy.com/2014/04/curse-dimensionality-affect-classification/

http://www.metacademy.org/

http://arxiv.org/abs/1405.0126

http://rs.io/2014/02/16/simulated-annealing-intuition.html

http://www.americanscientist.org/issues/id.16185,y.2014,no.3,content.true,page.1,css.print/issue.aspx

http://www.denizyuret.com/2014/02/machine-learning-in-5-pictures.html

http://en.wikipedia.org/wiki/General_game_playing Stanford annual competition

https://github.com/tadasv/chimp ChimDB

https://www.coursera.org/course/compdata Online class

http://www.datatau.com/ hacker news for data mining

http://simplystatistics.org/

http://cognon.net/

http://www.hutter1.net/ai/uaibook.htm AI BOOK

http://www.cs.toronto.edu/~mackay/itprnn/book.html MACKAY BOOK

http://guidetodatamining.com/

http://www.amazon.com/Building-Machine-Learning-Systems-Python/dp/1782161406

https://news.ycombinator.com/item?id=7149913

http://blog.statwing.com/easiest-data-analysis-mistake-to-make/

http://www.datamind.org/#/

http://www.data-science.tips/give-me-them-digits/decision-tree-and-random-forest

http://www.win-vector.com/blog/

http://radimrehurek.com/gensim/ Topic modelling in Python

http://precog.com/ analytics engine that natively handles JSON

http://logic.pdmi.ras.ru/~yura/internet.html

http://nlp.stanford.edu/IR-book/ BOOK

http://www.r-bloggers.com/the-guerilla-guide-to-r/ R guide

http://www.refsmmat.com/statistics/data-analysis.html

http://www.kickstarter.com/projects/jeffheaton/artificial-intelligence-for-humans-vol-1-fund-algo

http://www.dataminingblog.com/list-of-blogs/

https://www.siam.org/proceedings/

http://www.amazon.com/PRACTITIONERS-GUIDE-BUSINESS-ANALYTICS-Organization-2019s/dp/0071807594

http://parleys.com/play/51c2e0f3e4b0ed877035684f/chapter0/about ML, Scala, eBay

http://blog.markwatson.com/

Online Classes

http://work.caltech.edu/previous.html

http://online.stanford.edu/course/statistical-learning-winter-2014

https://www.youtube.com/watch?v=IxflKHX7aes&list=PLZSO_6-bSqHQCIYxE3ycGLXHMjK3XV7Iz&index=6

http://habrahabr.ru/post/189178/

http://www.machinelearning.ru/

http://guidetodatamining.com/

http://nborwankar.github.io/LearnDataScience/

http://videolectures.net/mlss07_teh_dp/ Dirichlet Process Video/PDF

http://videolectures.net/mlss07_rasmussen_bigp/ Bayes and Gaussian Processing

http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

https://news.ycombinator.com/item?id=5817713 Bayes

http://www.knime.org/ - a user-friendly graphical workbench for the entire analysis process: data access, data transformation, initial investigation, powerful predictive analytics, visualisation and reporting.

BI, OLAP and Analytics

http://en.wikipedia.org/wiki/Business_intelligence

http://en.wikipedia.org/wiki/Category:Online_analytical_processing

http://en.wikipedia.org/wiki/Comparison_of_OLAP_Servers

Books:

Mondrian in Action: Open source business analytics

Theophano Mitsa, "Temporal Data Mining"

Richard T. Snodgrass, "Developing Time-Oriented Database Applications in SQL" 1999

James Wu, Stephen Coggeshall , "Foundations of Predictive Analytics"

Zheng Alan Zhao, "Spectral Feature Selection for Data Mining"

Yunqian Ma, "Manifold Learning Theory and Applications"

Guozhu Dong, James Bailey, Contrast Data Mining: Concepts, Algorithms, and Applications

Fon Silvers, "Data Warehouse Designs: Achieving ROI with Market Basket Analysis and Time Variance

http://blog.cloudera.com/blog/2013/04/hadoop-stratified-randosampling-algorithm/

http://eferm.com/machine-learning-cheat-sheet/ cheat sheet

http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html

C++ machine learning

http://fidoproject.github.io/

http://sourceforge.net/projects/shark-project/

http://dlib.net/ml.html

http://tapkee.lisitsyn.me/

http://web.mit.edu/newsoffice/2013/machine-learning-algorithm-outperforms-predecessors-0529.html

http://egtheory.wordpress.com/2013/06/05/prediction-vs-understanding/

http://pauloortins.com/resources-to-become-a-ninja-machine-learning/

BOOK

http://www.amazon.com/Understanding-Complex-Datasets-Decompositions-Knowledge/dp/1584888326

https://leanpub.com/javaai Practical AI Intelligence Book

Random forest

http://blog.yhathq.com/posts/random-forests-in-python.html

https://medium.com/rants-on-machine-learning/the-unreasonable-effectiveness-of-random-forests-f33c3ce28883

http://www.rene-pickhardt.de/please-help-me-to-realize-my-web-science-massive-open-online-course/

http://biglearn.org/index.php/Papers

http://alex.smola.org/teaching/cmu2013-10-701/index.html

http://www.bigdatarepublic.com/author.asp?section_id=2809&doc_id=257527&

https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

http://www.youtube.com/watch?v=bobeo5kFz1g Bayes

http://www.youtube.com/watch?v=jKBwGlYb13w Big data algorithms

http://probabilistic-programming.org/wiki/Home

http://math.stackexchange.com/questions/3869/what-is-the-intuitive-relationship-between-svd-and-pca

Nikolenko: multiarmed bandits; SVD, bayes graphical models

http://habrahabr.ru/company/surfingbird/blog/168611/

http://habrahabr.ru/company/yandex/blog/229555/

http://habrahabr.ru/company/surfingbird/blog/169573/

http://habrahabr.ru/company/surfingbird/blog/176461/

http://habrahabr.ru/company/surfingbird/blog/177889/

http://habrahabr.ru/post/188244/

http://tm.durusau.net/ Blog

http://habrahabr.ru/post/175819/ Bolzman machine

http://habrahabr.ru/company/yandex/blog/175917/

http://conductrics.com/data-science-resources-2

http://conductrics.com/data-science-resources/

Clustering

https://en.wikipedia.org/wiki/Cluster_analysis

http://haifengl.github.io/smile/index.html#clustering

http://andrew.gibiansky.com/blog/machine-learning/k-nearest-neighbors-simplest-machine-learning/

http://varianceexplained.org/r/kmeans-free-lunch/

https://databricks.com/blog/2015/01/28/introducing-streaming-k-means-in-spark-1-2.html

http://www.bigdatanews.com/profiles/blogs/fast-clustering-algorithms-for-massive-datasets

http://grigory.us/blog/mapreduce-clustering/

http://www.galvanize.com/blog/introduction-k-means-cluster-analysis/#.Vk_C0xFViko

http://guidetodatamining.com/

http://www.wired.com/design/2013/01/data-viz-ayasdi-iris/

http://www.p-value.info/2012/11/free-datascience-books.html

http://zinkov.com/posts/2012-10-04-ml-book-reviews/

http://habrahabr.ru/post/164211/

http://www.lektorium.tv/course/?id=22851

http://shad.yandex.ru/lectures/machine_learning.xml

http://conductrics.com/data-science-resources/

http://gigaom.com/data/a-programmers-guide-to-big-data-12-tools-to-know/

http://probabilistic-programming.org/wiki/Home

http://habrahabr.ru/post/161301/

http://ailev.livejournal.com/1047765.html?style=mine&nc=17#comments

http://blog.videolectures.net/100-most-popular-machine-learning-talks-at-videolectures-net/

http://www.cs.uvm.edu/~icdm/algorithms/10Algorithms-08.pdf

http://www.theatlantic.com/business/print/2012/11/5-statistics-problems-that-will-change-the-way-you-see-the-world/265147/

http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/

http://www.gambit-project.org/doc/index.html C++/Python for Game theory

http://en.wikipedia.org/wiki/Vowpal_Wabbit

Inference Engine

http://research.microsoft.com/en-us/um/cambridge/projects/infernet/default.aspx Infer.NET

http://cs.ru.nl/~jorism/libDAI/

http://courses.cms.caltech.edu/cs155/ Probabilistic Graphical Models

Expectation propagation / Beleif propagation

http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Expectation%20Propagation.aspx

http://arxiv.org/abs/1212.2991 Dimple: open-source API for probabilistic modeling

Rule Engines

http://www.infoq.com/articles/Rule-Engines

http://en.wikipedia.org/wiki/Rete_algorithm

https://speakerdeck.com/peteris/logic-programming Logic plogrammig

http://www.aelag.com/147952673

http://k2company.com/blog/2012/09/06/toolbox-for-learning-machine-learning-and-data-science/

http://www.cs.yale.edu/homes/el327/

Web Analytics

http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/

https://github.com/blog/1112-data-at-github

https://developers.google.com/bigquery/

ROOT

http://en.wikipedia.org/wiki/ROOT

http://root.cern.ch/drupal/

Bayes

http://code.google.com/p/ourmine/wiki/LectureNaiveBayes

https://news.ycombinator.com/item?id=10843680

http://habrahabr.ru/post/219721/

http://habrahabr.ru/post/170545/

http://habrahabr.ru/post/170633/

http://www.greenteapress.com/thinkbayes/ Bayes Book

http://www.mimno.org/articles/hdp/ non-parametric bayes

http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage Bayes Book

http://www.machinelearning.ru/

http://www.mblondel.org/journal/

http://www.autonlab.org/tutorials/index.html

http://deeplearning.net/tutorials/

http://machinelearningjourney.blogspot.com/

http://news.ycombinator.com/item?id=3199718

http://habrahabr.ru/blogs/data_mining

http://metaoptimize.com/qa/questions/3163/good-machine-learning-blogs

http://en.wikipedia.org/wiki/Data_mining#Methods_and_algorithms

http://www.win-vector.com/blog/2009/08/a-demonstration-of-data-mining/

http://habrahabr.ru/blogs/algorithm/124978/#habracut

http://www.mblondel.org/journal/2010/10/31/kernel-perceptron-in-python/

http://work.caltech.edu/library/ Machine lerning Video Library

http://www.shogun-toolbox.org/ SHOGUN

http://www.slideshare.net/dscottbrown/vicarious-systems-at-singularity-summit-2011

Support Vector Machines

http://www.machinelearning.ru/wiki/index.php?title=SVM

https://beta.oreilly.com/learning/intro-to-svm

https://www.youtube.com/watch?v=_PwhiWxHK8o

http://www.machinalis.com/blog/support-vector-machines/

http://habrahabr.ru/post/202486/ kernel trick

http://rvlasveld.github.io/blog/2013/07/12/introduction-to-one-class-support-vector-machines/

http://www.win-vector.com/blog/2011/10/kernel-methods-and-support-vector-machines-de-mystified/

http://habrahabr.ru/blogs/data_mining/105220/

http://antilamer.livejournal.com/381249.html#comments

http://www.mblondel.org/journal/2010/09/19/support-vector-machines-in-python/

http://hunch.net/~large_scale_survey/

http://habrahabr.ru/blogs/algorithm/125614/#habracut Fussy Logic

http://valserb.wordpress.com/2011/08/02/understanding-hidden-markov-models/

http://en.wikipedia.org/wiki/Machine_Learning

http://metaoptimize.com/

http://amundblog.blogspot.com/2008/06/pragmatic-classification-of-classifiers.html

http://atbrox.com/2010/02/08/parallel-machine-learning-for-hadoopmapreduce-a-python-example/

http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1

http://arxiv.org/PS_cache/arxiv/pdf/1105/1105.1951v2.pdf

Web Data Scrapping

https://scraperwiki.com/docs/python/python_libraries/

http://getthedata.org/questions/122/what-tools-or-services-are-good-for-scraping-data-from-websites

http://openflights.org/data.html

http://services.faa.gov/docs/services/airport/

http://www.quora.com/What-is-the-best-way-to-learn-data-mining-in-one-month

http://www.reddit.com/r/MachineLearning/comments/hwyus/learning_ml_after_graduating/

http://mlg.ucd.ie/summer

Markov Chains and Monte Carlo

http://users.aims.ac.za/~ioana/

http://habrahabr.ru/post/241317/

http://videolectures.net/mlss09uk_murray_mcmc/

http://habrahabr.ru/blogs/algorithm/134954/ Language recognition

http://users.livejournal.com/_winnie/320534.html#comments

http://metaoptimize.com/qa/

DataMining online

http://vis.stanford.edu/wrangler/

http://www.google.com/publicdata/home

http://www.philwhln.com/how-to-get-experience-working-with-large-datasets

http://www.slate.com/id/2285354/pagenum/all/#p2

http://labs.slate.com/

http://habrahabr.ru/blogs/algorithm/134950/#habracut

Parrondo Paradox

http://io9.com/5861287/parrondos-paradox-winning-two-games-youre-guaranteed-to-lose

The supervised learning problem is to find an approximation to an unknown function given a set of labeled examples. For the generalized linear models (GLM), the usual goal is to minimize the sum of squared deviations of the observed values for the dependent variable from those predicted by the model.

- Locality-sensitive hashing (1998) - алгоритмы, позволяющие вести быстрый поиск "самых похожих" изображений, звуков и вообще "ситуаций" в сколь угодно больших словарях

- Gradient boosting (1999) - текущий де-факто стандарт для машинного обучения

- Viola-Jones framework (2001) - первое realtime распознавание "естественных" объектов (лиц, людей и т.п.)

http://www.quora.com/Machine-Learning/What-are-some-good-resources-for-learning-about-machine-learning

http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning

http://www.win-vector.com/blog/2009/08/a-demonstration-of-data-mining/

http://scikit-learn.sourceforge.net/

http://pybrain.org/

http://waffles.sourceforge.net/

http://metaoptimize.com/qa/questions/3053/python-machine-learning-packages

http://www.gaussianprocess.org/

http://www.dcs.gla.ac.uk/~girolami/Machine_Learning_Module_2006/

http://andriybuday.blogspot.com/2010/03/my-implementation-of-self-organizing.html

http://www.cis.temple.edu/~ingargio/cis587/readings/id3-c45.html

Data Vizualization:

http://tulip.labri.fr/TulipDrupal/

http://www.ggobi.org/

http://gephi.org/

http://jeromyanglim.blogspot.com/2009/06/learning-r-for-researchers-in.html R

http://www.hselab.org/machinery/content/create-sequence-plots-r-and-ggplot2-and-save-pdfs

Statistics

http://habrahabr.ru/post/208684/ generating normal distrib from uniform distrib

http://daithiocrualaoich.github.io/kolmogorov_smirnov/

http://statcourse.com/

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/

http://cscs.umich.edu/~crshalizi/weblog/cat_36-402.html

http://www.statsoft.com/textbook/

http://chance.amstat.org/

http://en.wikipedia.org/wiki/Boy_or_Girl_paradox

http://en.wikipedia.org/wiki/Simpson%27s_paradox

Bayes Classifier

http://commonsenseatheism.com/?p=13156

http://en.wikipedia.org/wiki/Naive_Bayes_classifier

http://ebiquity.umbc.edu/blogger/2010/12/07/naive-bayes-classifier-in-50-lines/

http://bionicspirit.com/blog/2012/02/09/howto-build-naive-bayes-classifier.html

http://davywybiral.blogspot.com/2011/04/naive-bayes-and-author-detection.html

http://habrahabr.ru/blogs/python/120194/

http://cnx.org/content/m10985/latest/

http://sciencehouse.wordpress.com/2010/11/11/bayesian-parameter-estimation/

http://cscs.umich.edu/~crshalizi/weblog/796.html

http://blog.moertel.com/articles/2010/12/20/more-on-the-evidence-of-a-single-coin-toss

http://en.wikipedia.org/wiki/Statistical_hypothesis_testing

The t-test is the most commonly used method to evaluate the differences in means between two groups. For example, the t-test can be used to test for a difference in test scores between a group of patients who were given a drug and a control group who received a placebo.

Nonparametric tests are sometimes called distribution free statistics because they do not require that the data fit a normal distribution. More generally, nonparametric tests require less restrictive assumptions about the data. Another important reason for using these tests is that they allow for the analysis of categorical as well as rank data.

Summary of statistical tests http://www.uwsp.edu/psych/stat/indexTests.htm