diff --git a/README.md b/README.md index 01f1640..cc04b5a 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ This repository contains a topic-wise curated list of Machine Learning and Deep If you want to contribute to this list, please read [Contributing Guidelines](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/contributing.md). -##Table of Contents +## Table of Contents - [Miscellaneous](#general) - [Interview Resources](#interview) - [Artificial Intelligence](#ai) @@ -44,8 +44,7 @@ If you want to contribute to this list, please read [Contributing Guidelines](ht - [Optimizations](#opt) - [Other Useful Tutorials](#other) - -##Miscellaneous +## Miscellaneous - [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning) - [A curated list of awesome data visualization libraries and resources.](https://github.com/fasouto/awesome-dataviz) - [An awesome Data Science repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience) @@ -63,22 +62,19 @@ If you want to contribute to this list, please read [Contributing Guidelines](ht - [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/) - [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) - -##Interview Resources +## Interview Resources - [How can a computer science graduate student prepare himself for data scientist interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews) - [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) - [FAQs about 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) - -##Artificial Intelligence +## Artificial Intelligence - [Awesome Artificial Intelligence (GitHub Repo)](https://github.com/owainlewis/awesome-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) - [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) - -##Genetic Algorithms +## Genetic Algorithms - [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) - [Simple Implementation of Genetic Algorithms in Python (Part 1)](http://outlace.com/Simple-Genetic-Algorithm-in-15-lines-of-Python/), [Part 2](http://outlace.com/Simple-Genetic-Algorithm-Python-Addendum/) - [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) @@ -87,8 +83,7 @@ If you want to contribute to this list, please read [Contributing Guidelines](ht - [Genetic Programming in Python (GitHub)](https://github.com/trevorstephens/gplearn) - [Genetic Alogorithms vs Genetic Programming (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 +## Statistics - [Stat Trek Website](http://stattrek.com/) - A dedicated website to teach yourselves Statistics - [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) - Learn Statistics using an application-centric programming approach - [Statistics for Hackers | Slides | @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - Slides by Jake VanderPlas @@ -102,8 +97,8 @@ If you want to contribute to this list, please read [Contributing Guidelines](ht - [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) - [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) - -##Useful Blogs + +## Useful Blogs - [Edwin Chen's Blog](http://blog.echen.me/) - A blog about Math, stats, ML, crowdsourcing, data science - [The Data School Blog](http://www.dataschool.io/) - Data science for beginners! - [ML Wave](http://mlwave.com/) - A blog for Learning Machine Learning @@ -121,8 +116,7 @@ If you want to contribute to this list, please read [Contributing Guidelines](ht - [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog - [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence - -##Resources on Quora +## Resources on Quora - [Most Viewed Machine Learning 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) @@ -132,20 +126,17 @@ If you want to contribute to this list, please read [Contributing Guidelines](ht - [Data Science FAQs on Quora](https://www.quora.com/topic/Data-Science/faq) - [Machine Learning FAQs on Quora](https://www.quora.com/topic/Machine-Learning/faq) - -##Kaggle Competitions WriteUp +## 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 +## 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 +## 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) @@ -154,9 +145,7 @@ If you want to contribute to this list, please read [Contributing Guidelines](ht - [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/) - - -##Linear Regression +## Linear Regression - [General](#general-) - [Assumptions of Linear Regression](http://pareonline.net/getvn.asp?n=2&v=8), [Stack Exchange](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) @@ -180,8 +169,7 @@ If you want to contribute to this list, please read [Contributing Guidelines](ht - [Regularization and Variable Selection via the Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - -##Logistic Regression +## Logistic Regression - [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression) - [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) @@ -189,14 +177,11 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) - [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 +## Model Validation using Resampling - [Resampling Explained](https://en.wikipedia.org/wiki/Resampling_(statistics)) - [Partioning data set in R](http://stackoverflow.com/questions/13536537/partitioning-data-set-in-r-based-on-multiple-classes-of-observations) - [Implementing hold-out Validaion in R](http://stackoverflow.com/questions/22972854/how-to-implement-a-hold-out-validation-in-r), [2](http://www.gettinggeneticsdone.com/2011/02/split-data-frame-into-testing-and.html) - - - [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) @@ -212,20 +197,14 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a.pdf) - [CV for detecting and preventing Overfitting](http://www.autonlab.org/tutorials/overfit10.pdf) - [How does CV overcome the Overfitting Problem](http://stats.stackexchange.com/questions/9053/how-does-cross-validation-overcome-the-overfitting-problem) - - - - - [Bootstrapping](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)) - [Why Bootstrapping Works?](http://stats.stackexchange.com/questions/26088/explaining-to-laypeople-why-bootstrapping-works) - [Good Animation](https://www.stat.auckland.ac.nz/~wild/BootAnim/) - [Example of Bootstapping](http://statistics.about.com/od/Applications/a/Example-Of-Bootstrapping.htm) - [Understanding Bootstapping for Validation and Model Selection](http://stats.stackexchange.com/questions/14516/understanding-bootstrapping-for-validation-and-model-selection?rq=1) - [Cross Validation vs Bootstrap to estimate prediction error](http://stats.stackexchange.com/questions/18348/differences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio), [Cross-validation vs .632 bootstrapping to evaluate classification performance](http://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance) - - -##Deep Learning +## Deep Learning - [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) - [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) - [Interesting Deep Learning and NLP Projects (Stanford)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) @@ -257,8 +236,6 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - Neural Machine Translation - [Introduction to Neural Machine Translation with GPUs (part 1)](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) - [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](http://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-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) @@ -296,9 +273,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [TensorFlow Examples for Beginners](https://github.com/aymericdamien/TensorFlow-Examples) - [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow) - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) - - - + - 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) @@ -311,8 +286,6 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [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 (GitHub Repo)](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/) @@ -340,13 +313,9 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [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 - [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) - [Another Good Tutorial](http://deeplearning.net/tutorial/rbm.html) @@ -355,16 +324,11 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [RBMs in R](https://github.com/zachmayer/rbm) - [Deep Belief Networks Tutorial](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 Tutorial](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 (GitHub)](https://github.com/kjw0612/awesome-deep-vision) - [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html) @@ -376,9 +340,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [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 +## 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/) @@ -387,7 +349,6 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) - [Bag of Words](https://en.wikipedia.org/wiki/Bag-of-words_model) - [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) - - [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) - [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) @@ -401,8 +362,6 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [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) @@ -428,14 +387,12 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [What would Shakespeare say (NLP Tutorial)](https://gigadom.wordpress.com/2015/10/02/natural-language-processing-what-would-shakespeare-say/) - [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) - -##Computer Vision +## 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 +## Support Vector Machine - [Highest Voted Questions about SVMs on Cross Validated](http://stats.stackexchange.com/questions/tagged/svm) - [Help me Understand SVMs!](http://stats.stackexchange.com/questions/3947/help-me-understand-support-vector-machines) - [SVM in Layman's terms](https://www.quora.com/What-does-support-vector-machine-SVM-mean-in-laymans-terms) @@ -463,13 +420,11 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Classifier Classification with Platt's Scaling](http://fastml.com/classifier-calibration-with-platts-scaling-and-isotonic-regression/) - -##Reinforcement Learning +## Reinforcement Learning - [Awesome Reinforcement Learning (GitHub)](https://github.com/aikorea/awesome-rl) - [RL Tutorial Part 1](http://outlace.com/Reinforcement-Learning-Part-1/), [Part 2](http://outlace.com/Reinforcement-Learning-Part-2/) - -##Decision Trees +## Decision Trees - [Wikipedia Page - Lots of Good Info](https://en.wikipedia.org/wiki/Decision_tree_learning) - [FAQs about Decision Trees](http://stats.stackexchange.com/questions/tagged/cart) - [Brief Tour of Trees and Forests](http://statistical-research.com/a-brief-tour-of-the-trees-and-forests/) @@ -511,7 +466,6 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Bayesian Learning in Probabilistic Decision Trees](http://www.stats.org.uk/bayesian/Jordan.pdf) - [Probabilistic Trees Research Paper](http://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf) - ##Random Forest / Bagging - [Awesome Random Forest (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) @@ -525,8 +479,8 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [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 - [Boosting for Better Predictions](http://www.datasciencecentral.com/profiles/blogs/boosting-algorithms-for-better-predictions) - [Boosting Wikipedia Page](https://en.wikipedia.org/wiki/Boosting_(machine_learning)) - [Introduction to Boosted Trees | Tianqi Chen](https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf) @@ -549,8 +503,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [adaBag R package](https://cran.r-project.org/web/packages/adabag/adabag.pdf) - [Tutorial](http://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf) - -##Ensembles +## Ensembles - [Wikipedia Article on Ensemble Learning](https://en.wikipedia.org/wiki/Ensemble_learning) - [Kaggle Ensembling Guide](http://mlwave.com/kaggle-ensembling-guide/) - [The Power of Simple Ensembles](http://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/) @@ -564,15 +517,13 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [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 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) - -##Vapnik–Chervonenkis Dimension +## Vapnik–Chervonenkis Dimension - [Wikipedia article on VC Dimension](https://en.wikipedia.org/wiki/VC_dimension) - [Intuitive Explanantion of VC Dimension](https://www.quora.com/Explain-VC-dimension-and-shattering-in-lucid-Way) - [Video explaining VC Dimension](https://www.youtube.com/watch?v=puDzy2XmR5c) @@ -581,8 +532,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Do ensemble techniques increase VC-dimension?](http://stats.stackexchange.com/questions/78076/do-ensemble-techniques-increase-vc-dimension) - -##Bayesian Machine Learning +## 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) @@ -591,9 +541,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Kalman & Bayesian Filters in Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) - [Markov Chain Wikipedia Page](https://en.wikipedia.org/wiki/Markov_chain) - - -##Semi Supervised Learning +## Semi Supervised Learning - [Wikipedia article on Semi Supervised Learning](https://en.wikipedia.org/wiki/Semi-supervised_learning) - [Tutorial on Semi Supervised Learning](http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.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) @@ -602,10 +550,7 @@ Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a. - [Unsupervised, Supervised and Semi Supervised learning](http://stats.stackexchange.com/questions/517/unsupervised-supervised-and-semi-supervised-learning) - [Research 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 +## 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) @@ -615,6 +560,5 @@ Learning](http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Wright-Steve/s - [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) - -##Other Tutorials +## Other Tutorials - For a collection of Data Science Tutorials using R, please refer to [this list](https://github.com/ujjwalkarn/DataScienceR).