This repository contains codes and other resources related to Machine Learning.

##Contents

• General Stuff
• Interview Resources
• Artificial Intelligence
• Genetic Algorithms
• Statistics
• Useful Blogs
• Resources on Quora
• Resources on Kaggle
• Cheat Sheets
• Classification
• k Nearest Neighbors
• Linear Discriminant Analysis
• Linear Regression
• Logistic Regression
• Model Validation using Resampling
  • Cross Validation
  • Bootstraping
• Hyperparameter Optimization
• Bias Variance Tradeoff
• Feature Learning
• Deep Learning
  • Frameworks
    • Theano
    • Torch
    • Caffe
    • TensorFlow
  • Feed Forward Networks
  • Recurrent Neural Nets, LSTM, GRU
  • Restricted Boltzmann Machine, DBNs
  • Autoencoders
  • Convolution Neural Nets
• Natural Language Processing
  • Topic Modeling, LDA
  • Word2Vec
• Computer Vision
• Support Vector Machine
• Reinforcement Learning
• Decision Trees
• Random Forest / Bagging
• Boosting
• Ensembles
• Stacking Models
• VC Dimension
• Clustering
• Bayesian Machine Learning
• Time Series Forecasting
• Semi Supervised Learning
• Optimizations
• Applications

##General Stuff - [**A curated list of awesome Machine Learning frameworks, libraries and software**](https://github.com/josephmisiti/awesome-machine-learning) - [**An awesome Data Science repository to learn and apply for real world problems**](https://github.com/okulbilisim/awesome-datascience) - [**The Open Source Data Science Masters**](http://datasciencemasters.org/) - [**ML FAQs Cross Validated**](http://stats.stackexchange.com/questions/tagged/machine-learning) - [**ML University Courses**](https://github.com/prakhar1989/awesome-courses#machine-learning) - [**Machine Learning algorithms that you should always have a strong understanding of**](https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why) - [*List of ML Concepts*](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts) - [Slides on Several Topics](http://www.slideshare.net/pierluca.lanzi/presentations) - [Predictive Analytics](https://en.wikipedia.org/wiki/Predictive_analytics) - [MIT Machine Learning Lecture Slides (MUST SEE!)](http://www.ai.mit.edu/courses/6.867-f04/lectures.html) - [Awesome Machine Learning frameworks, libraries and software](https://github.com/ujjwalkarn/awesome-machine-learning) - [Comparing Supervised Learning Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) - [Useful Data Science Resources](http://www.dataschool.io/learning-data-science-fundamentals/) - [The Data School Blog](http://www.dataschool.io/) - [Hadley Wickham's AMA](https://www.reddit.com/r/dataisbeautiful/comments/3mp9r7/im_hadley_wickham_chief_scientist_at_rstudio_and/) - [ML mistakes to avoid](https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l) - [ML University Courses Awesome](https://github.com/prakhar1989/awesome-courses#machine-learning) - [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/) - [Intro to ML](http://alex.smola.org/teaching/10-701-15/index.html) - [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) ##Interview Resources - [How can a computer science graduate student prepare himself for data scientist/machine learning interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews) - [Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq) - [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) - [Data Science FAQ](https://www.quora.com/topic/Data-Science/faq) - [How do I learn ML](https://www.quora.com/How-do-I-learn-machine-learning-1) - [ML FAQ](https://www.quora.com/topic/Machine-Learning/faq), [Deep Learning FAQ](https://www.quora.com/topic/Deep-Learning/faq) ##Artificial Intelligence - [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) - [Udacity Course | Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) ##Genetic Algorithms - [Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) - [Simple Implementation Part 1](http://outlace.com/Simple-Genetic-Algorithm-in-15-lines-of-Python/), [Part 2](http://outlace.com/Simple-Genetic-Algorithm-Python-Addendum/) - [GAs vs ANNs](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) - [Genetic Algorithms in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html) - [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming) - [Genetic Programming in Python | GitHub](https://github.com/trevorstephens/gplearn) - [GA vs GP Quora](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming) ##Statistics - [Stat Trek Website](http://stattrek.com/) - [STATISTICS USING PYTHON! | GITHUB](https://github.com/rouseguy/intro2stats) - [THINK STATS BOOK](http://greenteapress.com/thinkstats/), [THINK BAYES BOOK | BAYESIAN STATITICS MADE SIMPLE](http://greenteapress.com/thinkbayes/) - [Statistics for Hackers | Slides | @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - [Online Stat Book](http://onlinestatbook.com/2/index.html) - [Sampling Distributions Explained](http://stattrek.com/sampling/sampling-distribution.aspx) - Tutorials - [AP Statistics Tutorial](http://stattrek.com/tutorials/ap-statistics-tutorial.aspx) - [Statistics and Probability Tutorial](http://stattrek.com/tutorials/statistics-tutorial.aspx) - [Matrix Algebra Tutorial](http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx) - [What is an Unbiased Estimator](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/), [Unbiased Estimate](http://stattrek.com/statistics/dictionary.aspx?definition=Unbiased_estimator) - [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) - [QQ Plots](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) - [*Nonparametric statistics*](https://en.wikipedia.org/wiki/Nonparametric_statistics) ##Useful Blogs - [Edwin Chen's Blog](http://blog.echen.me/) - [ML Wave AWESOME!](http://mlwave.com/) - [ML Daniel Nouri](http://danielnouri.org/notes/) - [Deep Learning Awesome | Andrej Karpathy](http://karpathy.github.io/) - [Awesome Neural Networks Blog](http://colah.github.io/) - [Deep Learning Alex Minnaar](http://alexminnaar.com/) - [Statistically Significant](http://andland.github.io/) - [Simply Statistics](http://simplystatistics.org/) - [Yanir Seroussi's Blog](http://yanirseroussi.com/) - [fastML](http://fastml.com/) - [Trevor Stephens Blog](http://trevorstephens.com/) - [no free hunch | kaggle](http://blog.kaggle.com/) - [Statistical Modeling etc](http://andrewgelman.com/) - [A Quantitative Journey | outlace](http://outlace.com/) - [r4stats](http://r4stats.com/) - [Adam Paszke's Blog](https://apaszke.github.io/posts.html) - [Variance Explained Blog](http://varianceexplained.org/) - [Lars Eidnes' blog](http://larseidnes.com/) - [tedunderwook](http://tedunderwood.com/) - [AI Junkie](http://www.ai-junkie.com/) - [neuralniche](http://neuralniche.com/) - [Garbled Notes](http://www.chioka.in/) - [Beating the Benchmark](http://beatingthebenchmark.blogspot.in/) - [DnI Institute](http://dni-institute.in/blogs/) - [Galvanize](http://www.galvanize.com/blog/) - [Manuel's Blog](http://amunategui.github.io/) - [Philippe Adjiman](http://www.philippeadjiman.com/blog/) - [Win Vector Blog](http://www.win-vector.com/blog/popular-articles/) - [BzST](http://www.bzst.com/) ##Resources on Quora - [**Most Viewed ML writers**](https://www.quora.com/topic/Machine-Learning/writers) - [Data Science Topic on Quora](https://www.quora.com/Data-Science) - [William Chen's Answers](https://www.quora.com/William-Chen-6/answers) - [Michael Hochster's Answers](https://www.quora.com/Michael-Hochster/answers) - [Ricardo Vladimiro's Answers](https://www.quora.com/Ricardo-Vladimiro-1/answers) - [Storytelling with Statistics](https://datastories.quora.com/) ##Kaggle Competitions WriteUp - [How to almost win Kaggle Competitions](http://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/) - [Convolution Neural Networks for EEG detection](http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/) - [Facebook Recruiting III Explained](http://alexminnaar.com/tag/kaggle-competitions.html) - [Predicting CTR with Online ML](http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/) ##Cheat Sheets - [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), [Source](http://www.wzchen.com/probability-cheatsheet/) - [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) ##Classification - [Does Balancing Classes Improve Classifier Performance?](http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/) - [**What is Deviance?**](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) - [**When to choose which machine learning classifier?**](http://stackoverflow.com/questions/2595176/when-to-choose-which-machine-learning-classifier) - [**What are the advantages of different classification algorithms?**](https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms) - [ROC and AUC Explained](http://www.dataschool.io/roc-curves-and-auc-explained/) - [**An introduction to ROC analysis**](https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf) - [Simple guide to confusion matrix terminology](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) ##k Nearest Neighbors - [Saving and incrementally updating nearest-neighbor model in R](http://stackoverflow.com/questions/12202263/saving-and-incrementally-updating-nearest-neighbor-model-in-r) - [Intro](http://www.scholarpedia.org/article/K-nearest_neighbor), [2](http://www.statsoft.com/textbook/k-nearest-neighbors) - [Classification](http://www.saedsayad.com/k_nearest_neighbors.htm), [Regression](http://www.saedsayad.com/k_nearest_neighbors_reg.htm) ##Linear Discriminant Analysis - [Wiki](https://en.wikipedia.org/wiki/Linear_discriminant_analysis) ##Linear Regression - [General](#general-) - [Assumptions of Linear Regression](http://pareonline.net/getvn.asp?n=2&v=8), [MUST SEE!](http://stats.stackexchange.com/questions/16381/what-is-a-complete-list-of-the-usual-assumptions-for-linear-regression) - [Linear Regression Comprehensive Resource](http://people.duke.edu/~rnau/regintro.htm) - [Applying and Interpreting Linear Regression](http://www.dataschool.io/applying-and-interpreting-linear-regression/) - [What does having constant variance in a linear regression model mean?](http://stats.stackexchange.com/questions/52089/what-does-having-constant-variance-in-a-linear-regression-model-mean/52107?stw=2#52107) - [Difference between linear regression on y with x and x with y](http://stats.stackexchange.com/questions/22718/what-is-the-difference-between-linear-regression-on-y-with-x-and-x-with-y?lq=1) - [Is linear regression valid when the dependant variable is not normally distributed?](http://www.researchgate.net/post/Is_linear_regression_valid_when_the_outcome_dependant_variable_not_normally_distributed) - Multicollinearity and VIF - [Dummy Variable Trap | Multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity) - [Dealing with multicollinearity using VIFs](http://jonlefcheck.net/2012/12/28/dealing-with-multicollinearity-using-variance-inflation-factors/)

• Significance Tests

  • t-test vs z-test, 2

• Residual Analysis

  • Interpreting plot.lm() in R
  • How to interpret a QQ plot?
  • Interpreting Residuals vs Fitted Plot

• Outliers

  • How should outliers be dealt with?

• Elastic Net

  • Regularization and Variable Selection via the Elastic Net

##Logistic Regression - [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression) - [**What is Deviance?**](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) - [Geometric Intuition of Logistic Regression](http://florianhartl.com/logistic-regression-geometric-intuition.html) - [Obtaining predicted categories (choosing threshold)](http://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit) - [Residuals in logistic regression (Awesome!)](http://stats.stackexchange.com/questions/1432/what-do-the-residuals-in-a-logistic-regression-mean) - [Difference between logit and probit models](http://stats.stackexchange.com/questions/20523/difference-between-logit-and-probit-models#30909), [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression), [Probit Model Wiki](https://en.wikipedia.org/wiki/Probit_model) - [Pseudo R2 for Logistic Regression](http://stats.stackexchange.com/questions/3559/which-pseudo-r2-measure-is-the-one-to-report-for-logistic-regression-cox-s), [How to calculate](http://stats.stackexchange.com/questions/8511/how-to-calculate-pseudo-r2-from-rs-logistic-regression), [Other Details](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm) ##Model Validation using Resampling

• Resampling Explained
• Partioning data set in R
• Implementing hold-out Validaion in R, 2

- [Cross Validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) - [Training with Full dataset after CV?](http://stats.stackexchange.com/questions/11602/training-with-the-full-dataset-after-cross-validation) - [Which CV method is best?](http://stats.stackexchange.com/questions/103459/how-do-i-know-which-method-of-cross-validation-is-best) - [Variance Estimates in k-fold CV](http://stats.stackexchange.com/questions/31190/variance-estimates-in-k-fold-cross-validation) - [Is CV a subsitute for Validation Set?](http://stats.stackexchange.com/questions/18856/is-cross-validation-a-proper-substitute-for-validation-set) - [*Choice of k in k-fold CV*](http://stats.stackexchange.com/questions/27730/choice-of-k-in-k-fold-cross-validation) - [CV for ensemble learning](http://stats.stackexchange.com/questions/102631/k-fold-cross-validation-of-ensemble-learning) - [k-fold CV in R](http://stackoverflow.com/questions/22909197/creating-folds-for-k-fold-cv-in-r-using-caret) - [Cv Africa Soil Prediction](https://www.kaggle.com/c/afsis-soil-properties/forums/t/10158/training-set-cross-validation/55484) - [Good Resources](http://www.chioka.in/tag/cross-validation/)

<a name="boot" />

• Bootstrapping
  • Why Bootsrapping Works!
  • Good Animation
  • Paper
  • Example of Bootstapping
  • Understanding Bootstapping for Validation and Model Selection
  • Cross Validation vs Bootstrap to estimate prediction error, Cross-validation vs .632 bootstrapping to evaluate classification performance
  • Overfitting and Cross Validation
    • Preventing Overfitting the Cross Validation Data | Andrew Ng
    • Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
    • CV for detecting and preventing Overfitting
    • How does CV overcome the Overfitting Problem

##Hyperparameter Optimization - [Wiki](https://en.wikipedia.org/wiki/Hyperparameter_optimization) ##Bias Variance Tradeoff - [Wiki](https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff) - [Good Explanation](http://scott.fortmann-roe.com/docs/BiasVariance.html) ##Feature Learning - [Wiki](https://en.wikipedia.org/wiki/Feature_learning) ##Deep Learning - [**A curated list of awesome Deep Learning tutorials, projects and communities**](https://github.com/ChristosChristofidis/awesome-deep-learning) - [LOTS OF RESOURCES!](http://deeplearning4j.org/documentation.html) - [**Interesting Deep Learning NLP Projects Stanford**](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) - [**Core Concepts**](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) - [**Understanding Natural Language with Deep Neural Networks Using Torch**](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) - [**Stanford Deep Learning Tutorial**](http://ufldl.stanford.edu/tutorial/) - [Google+ Page](https://plus.google.com/communities/112866381580457264725) - [Recent Reddit AMAs](http://deeplearning.net/2014/11/22/recent-reddit-amas-about-deep-learning/), [2](https://www.reddit.com/r/IAmA/comments/3mdk9v/we_are_google_researchers_working_on_deep/) - [Where to Learn Deep Learning](http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html) - [Where are the Deep Learning 2](http://www.datacommunitydc.org/blog/2014/05/deep-learning-courses/) - [Deep Learning nvidia concepts](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) - [NN FAQs GOOD! (copy paste link)](ftp://ftp.sas.com/pub/neural/FAQ3.html#A_hu) - [INTRO TO DEEP LEARNING USING PYTHON | GITHUB](https://github.com/rouseguy/intro2deeplearning), [Good Intro Slides](https://speakerdeck.com/bargava/introduction-to-deep-learning) - [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http://videolectures.net/deeplearning2015_montreal/) - [Software](http://deeplearning.net/software_links/) - [Hacker's guide to Neural Nets](http://karpathy.github.io/neuralnets/) - [Awesome Neural Networks Blog](http://colah.github.io/) - [Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html) - [Geoff Hinton Youtube Deep Learning](https://www.youtube.com/watch?v=IcOMKXAw5VA) - [Projects to contribute to](https://github.com/karpathy) - [Awesome Reading List](http://deeplearning.net/reading-list/) - [Deep Learning Comprehensive Website](http://deeplearning.net/), [Software](http://deeplearning.net/software_links/) - [deeplearning Tutorials](http://deeplearning4j.org/) - [**AWESOME! Deep Learning Tutorial**](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) - [Deep Learning Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html) - [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) - [SVMs > ANNs](http://stackoverflow.com/questions/6699222/support-vector-machines-better-than-artificial-neural-networks-in-which-learn?rq=1), [ANNs > SVMs](http://stackoverflow.com/questions/11632516/what-are-advantages-of-artificial-neural-networks-over-support-vector-machines) - [Train, Validation & Test in ANNs](http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ) - [ANN Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) - [FAQs Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) - [Deep Learning Tutorials deeplearning.net](http://deeplearning.net/tutorial/index.html) - [Kaggle Deep Learn](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-4-comparing-deep-and-non-deep-learning-methods)

• Neural Machine Translation
  • Introduction to Neural Machine Translation with GPUs (part 1), Part 2, Part 3
  • Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning

- Deep Learning Frameworks - [Torch vs. Theano](http://fastml.com/torch-vs-theano/) - [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html) - [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/)

- [Theano](https://en.wikipedia.org/wiki/Theano_(software))
    - [Website](http://deeplearning.net/software/theano/) 
    - [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/)
    - [Theano Tutorial](http://outlace.com/Beginner-Tutorial-Theano/)
    - [Good Theano Tutorial](http://deeplearning.net/software/theano/tutorial/)
    - [Logistic Regression using Theano for classifying digits](http://deeplearning.net/tutorial/logreg.html#logreg)
    - [MLP using Theano](http://deeplearning.net/tutorial/mlp.html#mlp)
    - [CNN using Theano](http://deeplearning.net/tutorial/lenet.html#lenet)
    - [RNNs using Theano](http://deeplearning.net/tutorial/rnnslu.html#rnnslu)
    - [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm)
    - [RBM using Theano](http://deeplearning.net/tutorial/rbm.html#rbm)
    - [DBNs using Theano](http://deeplearning.net/tutorial/DBN.html#dbn)
    - [All Codes](https://github.com/lisa-lab/DeepLearningTutorials)
    
- [Torch](http://torch.ch/)
    - [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials)
    - [Intro to Torch](http://ml.informatik.uni-freiburg.de/_media/teaching/ws1415/presentation_dl_lect3.pdf)
    - [ML using Torch Oxford Univ](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/), [Code](https://github.com/oxford-cs-ml-2015)
    - [Torch Internals Overview](https://apaszke.github.io/torch-internals.html)     
    - [Torch Cheatsheet](https://github.com/torch/torch7/wiki/Cheatsheet)
    - [**Understanding Natural Language with Deep Neural Networks Using Torch**](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/)
    
- Caffe
    - [Deep Learning for Computer Vision with Caffe and cuDNN](http://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/)

- TensorFlow (by Google)
    - [Website](http://tensorflow.org/)
    - [Other Info](https://www.linkedin.com/pulse/googles-deep-learning-core-framework-tensor-flow-open-lin-sun)
    
    

- [DistBelief Framework Implementation -- no GPU needed](http://alexminnaar.com/implementing-distbelief-with-akka.html)

- Feed Forward Networks - [Implementing a Neural Network from scratch](http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/), [Code](https://github.com/dennybritz/nn-from-scratch) - [Speeding up your Neural Network with Theano and the gpu](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/), [Code](https://github.com/dennybritz/nn-theano) - [Basic ANN Theory](https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/) - [Role of Bias in Neural Networks](http://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks) - [Choosing number of hidden layers and nodes](http://stackoverflow.com/questions/3345079/estimating-the-number-of-neurons-and-number-of-layers-of-an-artificial-neural-ne),[2](http://stackoverflow.com/questions/10565868/multi-layer-perceptron-mlp-architecture-criteria-for-choosing-number-of-hidde?lq=1),[3](http://stackoverflow.com/questions/9436209/how-to-choose-number-of-hidden-layers-and-nodes-in-neural-network/2#) - [Backpropagation Explained](http://home.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html) - [ANN implemented in C++ | AI Junkie](http://www.ai-junkie.com/ann/evolved/nnt6.html) - [Simple Implementation](http://stackoverflow.com/questions/15395835/simple-multi-layer-neural-network-implementation) - [NN for Beginners](http://www.codeproject.com/Articles/16419/AI-Neural-Network-for-beginners-Part-of) - [Regression and Classification with NNs (Slides)](http://www.autonlab.org/tutorials/neural13.pdf) - [Another Intro](http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html) - Recurrent and LSTM Networks - [**awesome-rnn: list of resources**](https://github.com/kjw0612/awesome-rnn) - [Recurrent Neural Net Tutorial Part 1](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/), [Part 2] (http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3] (http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/) - [NLP RNN Representations](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) - [The Unreasonable effectiveness of RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/), [Torch Code](https://github.com/karpathy/char-rnn), [Python Code](https://gist.github.com/karpathy/d4dee566867f8291f086) - [Intro to RNN](http://deeplearning4j.org/recurrentnetwork.html), [LSTM](http://deeplearning4j.org/lstm.html) - [An application of RNN](http://hackaday.com/2015/10/15/73-computer-scientists-created-a-neural-net-and-you-wont-believe-what-happened-next/) - [Optimizing RNN Performance](http://svail.github.io/) - [Must see](http://outlace.com/page/2/) - [Simple RNN](http://outlace.com/Simple-Recurrent-Neural-Network/) - [Auto-Generating Clickbait with RNN](http://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/) - [Sequence Learning using RNN (Slides)](http://www.slideshare.net/indicods/general-sequence-learning-with-recurrent-neural-networks-for-next-ml) - [Machine Translation using RNN (Paper)](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf) - [**MUSIC GENERATION USING RNNs | THEANO & KERAS**](https://github.com/MattVitelli/GRUV) - [**USING RNN TO CREATE ON-THE-FLY DIALOGUE | KERAS**](http://neuralniche.com/post/tutorial/) - Long Short Term Memory (LSTM) - [Understanding LSTM Networks AWESOME](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) - [LSTM explained](https://apaszke.github.io/lstm-explained.html) - [LSTM](http://deeplearning4j.org/lstm.html) - [Implementing LSTM from scratch](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/), [Python/Theano code](https://github.com/dennybritz/rnn-tutorial-gru-lstm) - [Torch Code](https://github.com/karpathy/char-rnn), [Torch](https://github.com/apaszke/kaggle-grasp-and-lift) - [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm) - [**Deep Learning for Visual Q&A | LSTM | CNN**](http://avisingh599.github.io/deeplearning/visual-qa/), [Code](https://github.com/avisingh599/visual-qa) - [Computer Responds to email | Google](http://googleresearch.blogspot.in/2015/11/computer-respond-to-this-email.html) - [**LSTM dramatically improves Google Voice Search**](http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html), [2](http://deeplearning.net/2015/09/30/long-short-term-memory-dramatically-improves-google-voice-etc-now-available-to-a-billion-users/) - [**Understanding Natural Language with Deep Neural Networks Using Torch**](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) - Gated Recurrent Units (GRU) - [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/) - [Recursive Neural Network (not Recurrent)](https://en.wikipedia.org/wiki/Recursive_neural_network) - [**Recursive Neural Tensor Network (RNTN)**](http://deeplearning4j.org/recursiveneuraltensornetwork.html) - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) - Restricted Boltzmann Machine - [**Good Beginner's Guide**](http://deeplearning4j.org/restrictedboltzmannmachine.html) - [Another Good Tutorial](http://deeplearning.net/tutorial/rbm.html) - [Awesome! Introduction to RBMs](http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/) - [*Hinton's Guide to Training RBMs*](https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf) - [RBMs in R](https://github.com/zachmayer/rbm) - [**Deep Belief Networks**](http://deeplearning4j.org/deepbeliefnetwork.html) - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) - Autoencoders: Unsupervised (applies BackProp after setting target = input) - [**Andrew Ng Sparse Autoencoders pdf**](https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf) - [Deep Autoencoders](http://deeplearning4j.org/deepautoencoder.html) - [Denoising Autoencoders](http://deeplearning.net/tutorial/dA.html), [Theano Code](http://deeplearning.net/tutorial/code/dA.py) - [Stacked Denoising Autoencoders](http://deeplearning.net/tutorial/SdA.html#sda) - Convolution Networks - [**awesome deep vision: list of resources**](https://github.com/kjw0612/awesome-deep-vision) - [Intro](http://deeplearning4j.org/convolutionalnets.html) - [**Understanding CNN for NLP**](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) - [Stanford Notes](http://vision.stanford.edu/teaching/cs231n/), [Codes](http://cs231n.github.io/), [GitHub](https://github.com/cs231n/cs231n.github.io) - [JavaScript Library (Browser Based)](http://cs.stanford.edu/people/karpathy/convnetjs/) - [Using CNNs to detect facial keypoints](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/) - [Deep learning to classify business photos at Yelp](http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-to-classify-business-photos-at-yelp.html) - [Interview with Yann LeCun | Kaggle](http://blog.kaggle.com/2014/12/22/convolutional-nets-and-cifar-10-an-interview-with-yan-lecun/) - [Visualising and Understanding CNNs](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) ##Natural Language Processing - [**A curated list of speech and natural language processing resources**](https://github.com/edobashira/speech-language-processing) - [**Understanding Natural Language with Deep Neural Networks Using Torch**](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) - [tf-idf explained](http://michaelerasm.us/tf-idf-in-10-minutes/) - [A Good Post](https://www.linkedin.com/pulse/what-topic-modeling-shailendra-s-hiring-data-scientists) - [**Interesting Deep Learning NLP Projects Stanford**](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) - [**NLP from Scratch | Google Paper**](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) - [Topic Modeling](https://en.wikipedia.org/wiki/Topic_model) - [LDA](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) - [A Good Post](https://www.linkedin.com/pulse/what-topic-modeling-shailendra-s-hiring-data-scientists) - [AWESOME LDA EXPLANATION!](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/). [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html) - [The LDA Buffet- Intuitive Explanation](http://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/) - [**Difference between LSI and LDA**](https://www.quora.com/Whats-the-difference-between-Latent-Semantic-Indexing-LSI-and-Latent-Dirichlet-Allocation-LDA) - [Original LDA Paper](https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf) - [alpha and beta in LDA](http://datascience.stackexchange.com/questions/199/what-does-the-alpha-and-beta-hyperparameters-contribute-to-in-latent-dirichlet-a) - [Intuitive explanation of the Dirichlet distribution](https://www.quora.com/What-is-an-intuitive-explanation-of-the-Dirichlet-distribution) - [Topic modeling made just simple enough](http://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/) - [Online LDA](http://alexminnaar.com/online-latent-dirichlet-allocation-the-best-option-for-topic-modeling-with-large-data-sets.html), [Online LDA with Spark](http://alexminnaar.com/distributed-online-latent-dirichlet-allocation-with-apache-spark.html) - [LDA in Scala](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-i-the-theory.html), [Part 2](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-ii-the-code.html) - [Segmentation of Twitter Timelines via Topic Modeling](http://alexperrier.github.io/jekyll/update/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html) - [Topic Modeling of Twitter Followers](http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html) - word2vec - [Google word2vec](https://code.google.com/p/word2vec/) - [Bag of Words Model Wiki](https://en.wikipedia.org/wiki/Bag-of-words_model) - [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) - [Skip Gram Model Tutorial](http://alexminnaar.com/word2vec-tutorial-part-i-the-skip-gram-model.html), [CBoW Model](http://alexminnaar.com/word2vec-tutorial-part-ii-the-continuous-bag-of-words-model.html) - [Word Vectors Kaggle Tutorial Python](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors), [Part 2](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors) - [Making sense of word2vec](http://rare-technologies.com/making-sense-of-word2vec/) - [deeplearning4j](http://deeplearning4j.org/word2vec.html) - [Quora word2vec](https://www.quora.com/How-does-word2vec-work) - [Quora 1](https://www.quora.com/What-are-the-continuous-bag-of-words-and-skip-gram-architectures-in-laymans-terms), [2](https://www.quora.com/What-is-the-difference-between-the-Bag-of-Words-model-and-the-Continuous-Bag-of-Words-model), [3](https://www.quora.com/Is-skip-gram-negative-sampling-better-than-CBOW-NS-for-word2vec-If-so-why) - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html)

• Text Clustering

  • How string clustering works
  • Levenshtein distance for measuring the difference between two sequences
  • Text clustering with Levenshtein distances

• Language learning with NLP and reinforcement learning

• Kaggle Tutorial Bag of Words and Word vectors, Part 2, Part 3

• What would Shakespeare say

• A closer look at Skip Gram Modeling

##Computer Vision - [awesome computer vision ~ github](https://github.com/jbhuang0604/awesome-computer-vision) - [awesome deep vision ~ github](https://github.com/kjw0612/awesome-deep-vision) ##Support Vector Machine - [**Highest Voted Questions**](http://stats.stackexchange.com/questions/tagged/svm) - [**Understanding SVMs**](http://stats.stackexchange.com/questions/3947/help-me-understand-support-vector-machines) - [SVMs > ANNs](http://stackoverflow.com/questions/6699222/support-vector-machines-better-than-artificial-neural-networks-in-which-learn?rq=1), [ANNs > SVMs](http://stackoverflow.com/questions/11632516/what-are-advantages-of-artificial-neural-networks-over-support-vector-machines) - [Trees > SVMs](http://stats.stackexchange.com/questions/57438/why-is-svm-not-so-good-as-decision-tree-on-the-same-data) - [**Optimization Algorithms in Support Vector Machines**](http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf) ##Reinforcement Learning - [**awesome rl ~ github**](https://github.com/aikorea/awesome-rl) - [Tutorial Part 1](http://outlace.com/Reinforcement-Learning-Part-1/), [Part 2](http://outlace.com/Reinforcement-Learning-Part-2/) ##Decision Trees - [**Wikipedia**](https://en.wikipedia.org/wiki/Decision_tree_learning) - [**FAQs CV**](http://stats.stackexchange.com/questions/tagged/cart) - [Brief Tour](http://statistical-research.com/a-brief-tour-of-the-trees-and-forests/) - [Tree Based Models in R](http://www.statmethods.net/advstats/cart.html) - [How they work](http://www.aihorizon.com/essays/generalai/decision_trees.htm) - [Weak side of Decision Trees](http://stats.stackexchange.com/questions/1292/what-is-the-weak-side-of-decision-trees) - [Thorough Explanation and different algorithms](http://www.ise.bgu.ac.il/faculty/liorr/hbchap9.pdf) - [**What is entropy and information gain in the context of building decision trees**](http://stackoverflow.com/questions/1859554/what-is-entropy-and-information-gain) - [**Slides**](http://www.slideshare.net/pierluca.lanzi/machine-learning-and-data-mining-11-decision-trees) - [How do decision tree learning algorithms deal with missing values](http://stats.stackexchange.com/questions/96025/how-do-decision-tree-learning-algorithms-deal-with-missing-values-under-the-hoo) - [Using Surrogates to Improve Datasets with Missing Values](http://www.salford-systems.com/videos/tutorials/tips-and-tricks/using-surrogates-to-improve-datasets-with-missing-values) - [Good Article](https://www.mindtools.com/dectree.html) - [Are decision trees almost always binary trees?](http://stats.stackexchange.com/questions/12187/are-decision-trees-almost-always-binary-trees) - [*Pruning*](https://en.wikipedia.org/wiki/Pruning_(decision_trees)), [*Grafting*](https://en.wikipedia.org/wiki/Grafting_(decision_trees)) - [**What is Deviance?**](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) - Comparison of Different Algorithms - [CART vs CTREE](http://stats.stackexchange.com/questions/12140/conditional-inference-trees-vs-traditional-decision-trees) - [*Comparison of complexity or performance*](https://stackoverflow.com/questions/9979461/different-decision-tree-algorithms-with-comparison-of-complexity-or-performance) - [CHAID vs CART](http://stats.stackexchange.com/questions/61230/chaid-vs-crt-or-cart) , [CART vs CHAID](http://www.bzst.com/2006/10/classification-trees-cart-vs-chaid.html) - [Good Article](http://www.ftpress.com/articles/article.aspx?p=2248639&seqNum=11) - CART - [Recursive Partitioning](https://en.wikipedia.org/wiki/Recursive_partitioning) - [Explained](http://documents.software.dell.com/Statistics/Textbook/Classification-and-Regression-Trees) - [How to measure/rank “variable importance” when using CART?](http://stats.stackexchange.com/questions/6478/how-to-measure-rank-variable-importance-when-using-cart-specifically-using) - [Pruning a Tree in R](http://stackoverflow.com/questions/15318409/how-to-prune-a-tree-in-r) - [Does rpart use multivariate splits by default?](http://stats.stackexchange.com/questions/4356/does-rpart-use-multivariate-splits-by-default) - [FAQs](http://stats.stackexchange.com/questions/tagged/rpart) - CTREE - [party package](https://cran.r-project.org/web/packages/party/party.pdf) - [R Question](http://stackoverflow.com/questions/13772715/show-volume-in-each-node-using-ctree-plot-in-r) - [How to extract tree structure from ctree function?](http://stackoverflow.com/questions/8675664/how-to-extract-tree-structure-from-ctree-function) - CHAID - [Wiki](https://en.wikipedia.org/wiki/CHAID) - [Basic Intro](https://smartdrill.com/Introduction-to-CHAID.html) - [Step by step process](http://dni-institute.in/blogs/wp-content/uploads/2015/09/Decision-Tree-An-Illustration-of-Decision-Tree-Building-Process.pdf) - [CHAID and Exhaustive CHAID (copy paste link)](ftp://ftp.software.ibm.com/software/analytics/spss/support/Stats/Docs/Statistics/Algorithms/13.0/TREE-CHAID.pdf) - [Good Tutorial](http://www.statsoft.com/Textbook/CHAID-Analysis) - MARS - [Wiki](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines) - Probabilistic Decision Trees - [Bayesian Learning in Probabilistic Decision Trees](http://www.stats.org.uk/bayesian/Jordan.pdf) - [Probabilistic Trees](http://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf) ##Random Forest / Bagging - [**awesome rf ~ github**](https://github.com/kjw0612/awesome-random-forest) - [How to tune RF parameters in practice](https://www.kaggle.com/forums/f/15/kaggle-forum/t/4092/how-to-tune-rf-parameters-in-practice) - [Measures of variable importance in random forests](http://stats.stackexchange.com/questions/12605/measures-of-variable-importance-in-random-forests) - [Compare R-squared from two different Random Forest models](http://stats.stackexchange.com/questions/13869/compare-r-squared-from-two-different-random-forest-models) - [OOB Estimate Explained | RF vs LDA](https://stat.ethz.ch/education/semesters/ss2012/ams/slides/v10.2.pdf) - [Evaluating Random Forests for Survival Analysis Using Prediction Error Curve](http://www.jstatsoft.org/article/view/v050i11) - [*Why doesn't Random Forest handle missing values in predictors?*](http://stats.stackexchange.com/questions/98953/why-doesnt-random-forest-handle-missing-values-in-predictors) - [How to build random forests in R with missing (NA) values?](http://stackoverflow.com/questions/8370455/how-to-build-random-forests-in-r-with-missing-na-values) - [**FAQs CV**](http://stats.stackexchange.com/questions/tagged/random-forest), [FAQs SO](http://stackoverflow.com/questions/tagged/random-forest) - [*Obtaining knowledge from a random forest*](http://stats.stackexchange.com/questions/21152/obtaining-knowledge-from-a-random-forest) - [Some Questions for R implementation](http://stackoverflow.com/questions/20537186/getting-predictions-after-rfimpute), [2](http://stats.stackexchange.com/questions/81609/whether-preprocessing-is-needed-before-prediction-using-finalmodel-of-randomfore), [3](http://stackoverflow.com/questions/17059432/random-forest-package-in-r-shows-error-during-prediction-if-there-are-new-fact) ##Boosting - [Boosting for Better Predictions](http://www.datasciencecentral.com/profiles/blogs/boosting-algorithms-for-better-predictions) - [Boosting Wiki](https://en.wikipedia.org/wiki/Boosting_(machine_learning)) - [Introduction to Boosted Trees | Tianqi Chen](https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf) - Gradient Boosting Machine - [Gradiet Boosting Wiki](https://en.wikipedia.org/wiki/Gradient_boosting) - [Guidelines for GBM parameters in R](http://stats.stackexchange.com/questions/25748/what-are-some-useful-guidelines-for-gbm-parameters), [Strategy to set parameters](http://stats.stackexchange.com/questions/35984/strategy-to-set-the-gbm-parameters) - [Meaning of Interaction Depth](http://stats.stackexchange.com/questions/16501/what-does-interaction-depth-mean-in-gbm), [2](http://stats.stackexchange.com/questions/16501/what-does-interaction-depth-mean-in-gbm) - [Role of n.minobsinnode parameter of GBM in R](http://stats.stackexchange.com/questions/30645/role-of-n-minobsinnode-parameter-of-gbm-in-r) - [GBM in R](http://www.slideshare.net/mark_landry/gbm-package-in-r) - [FAQs GBM](http://stats.stackexchange.com/tags/gbm/hot) - [GBM vs xgboost](https://www.kaggle.com/c/higgs-boson/forums/t/9497/r-s-gbm-vs-python-s-xgboost)

• xgboost
  • xgboost tuning kaggle
  • xgboost vs gbm
  • xgboost survey
• AdaBoost
  • AdaBoost Wiki, Python Code
  • AdaBoost Sparse Input Support
  • adaBag R package
  • Tutorial

##Ensembles - [Wiki](https://en.wikipedia.org/wiki/Ensemble_learning) - [*KAGGLE ENSEMBLING GUIDE*](http://mlwave.com/kaggle-ensembling-guide/) - [*Power of Simple Ensembles*](http://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/) - [Ensemble Learning](http://machine-learning.martinsewell.com/ensembles/) - [Ensemble Learning Paper](http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/springerEBR09.pdf) - [Ensembling models with R](http://amunategui.github.io/blending-models/), [Ensembling Regression Models in R](http://stats.stackexchange.com/questions/26790/ensembling-regression-models), [**Intro to Ensembles in R**](http://www.vikparuchuri.com/blog/intro-to-ensemble-learning-in-r/) - [Ensembling Models with caret](http://stats.stackexchange.com/questions/27361/stacking-ensembling-models-with-caret) - [Bagging vs Boosting vs Stacking](http://stats.stackexchange.com/questions/18891/bagging-boosting-and-stacking-in-machine-learning) - [*Good Resources | Kaggle Africa*](https://www.kaggle.com/c/afsis-soil-properties/forums/t/10391/best-ensemble-references) - [Boosting vs Bagging](http://www.chioka.in/which-is-better-boosting-or-bagging/) - [Resources for learning how to implement ensemble methods](http://stats.stackexchange.com/questions/32703/resources-for-learning-how-to-implement-ensemble-methods) - [How are classifications merged in an ensemble classifier?](http://stats.stackexchange.com/questions/21502/how-are-classifications-merged-in-an-ensemble-classifier) ##Stacking Models - [**Stacking, Blending and Stacked Generalization**](http://www.chioka.in/stacking-blending-and-stacked-generalization/) - [Stacked Generalization (Stacking)](http://machine-learning.martinsewell.com/ensembles/stacking/) - [*Stacked Generalization: when does it work?*](http://www.ijcai.org/Past%20Proceedings/IJCAI-97-VOL2/PDF/011.pdf) - [Stacked Generalization Paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.1533&rep=rep1&type=pdf) ##VC Dimension - [Wiki](https://en.wikipedia.org/wiki/VC_dimension) - [*Intuitive Explanantion*](https://www.quora.com/Explain-VC-dimension-and-shattering-in-lucid-Way) - [Video](https://www.youtube.com/watch?v=puDzy2XmR5c) - [Intro](http://www.svms.org/vc-dimension/) - [FAQs CV](http://stats.stackexchange.com/questions/tagged/vc-dimension) - [Do ensemble techniques increase VC-dimension?](http://stats.stackexchange.com/questions/78076/do-ensemble-techniques-increase-vc-dimension) ##Clustering - [Cluster Analysis Wiki](https://en.wikipedia.org/wiki/Cluster_analysis) - [OPTICS Algo](https://en.wikipedia.org/wiki/OPTICS_algorithm) ##Bayesian Machine Learning - [**Bayesian Methods for Hackers (using pyMC)**](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - [Should all Machine Learning be Bayesian](http://videolectures.net/bark08_ghahramani_samlbb/) - [Tutorial on Bayesian Optimisation for Machine Learning](http://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf) - [Bayesian Reasoning and Deep Learning](http://blog.shakirm.com/2015/10/bayesian-reasoning-and-deep-learning/), [Slides](http://blog.shakirm.com/wp-content/uploads/2015/10/Bayes_Deep.pdf) - [BAYESIAN STATISTICS MADE SIMPLE](http://greenteapress.com/thinkbayes/) - [Kalman & Bayesian Filters in Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) - [Markov Chain](https://en.wikipedia.org/wiki/Markov_chain) ##Time Series Forecasting - [Forecasting Principles, Rob J Hyndman](http://robjhyndman.com/uwafiles/fpp-notes.pdf) - [DYNAMIC TIME WARPING + Time Series Classification & Clustering](http://nbviewer.ipython.org/github/alexminnaar/time-series-classification-and-clustering/blob/master/Time%20Series%20Classification%20and%20Clustering.ipynb), [GitHub](https://github.com/alexminnaar/time-series-classification-and-clustering) ##Semi Supervised Learning - [Wikipedia](https://en.wikipedia.org/wiki/Semi-supervised_learning) - [scikit-learn](http://scikit-learn.org/stable/modules/label_propagation.html) - [Tutorial](http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf) - [Graph Based SSL for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) - [Taxonomy](http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/taxo_[0].pdf) - [Video Tutorial Weka](https://www.youtube.com/watch?v=sWxcIjZFGNM) - [unsupervised-supervised-and-semi-supervised](http://stats.stackexchange.com/questions/517/unsupervised-supervised-and-semi-supervised-learning) - [Papers 1](http://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf), [2](http://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf), [3](http://icml.cc/2012/papers/616.pdf) ##Optimization - [Mean Variance Portfolio Optimization with R and Quadratic Programming](http://www.wdiam.com/2012/06/10/mean-variance-portfolio-optimization-with-r-and-quadratic-programming/?utm_content=buffer04c12&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer) - [**Algorithms for Sparse Optimization and Machine Learning**](http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Wright-Steve/sjw-ima12) - [**Optimization Algorithms in Machine Learning**](http://pages.cs.wisc.edu/~swright/nips2010/sjw-nips10.pdf), [**Video Lecture**](http://videolectures.net/nips2010_wright_oaml/) - [**Optimization Algorithms for Data Analysis**](http://www.birs.ca/workshops/2011/11w2035/files/Wright.pdf) - [Video Lectures on Optimization](http://videolectures.net/stephen_j_wright/) - [Optimization Algorithms in Support Vector Machines](http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf) - [The Interplay of Optimization and Machine Learning Research](http://jmlr.org/papers/volume7/MLOPT-intro06a/MLOPT-intro06a.pdf) ##Applications - [Detecting Fraudulent Skype Users via Machine Learning](http://www.dataschool.io/detecting-fraudulent-skype-users-via-machine-learning/),[Original Paper](http://research.microsoft.com/pubs/205472/aisec10-leontjeva.pdf)