====== [[wp>Machine learning]] approaches and algorithms ======
* [[http://videolectures.net/mlas06_mitchell_sla/]]
* [[http://www.intechopen.com/subject/artificial-intelligence|Free Artificial Intelligence Open Access Books]]
* [[http://www.cs.cmu.edu/~tom/NewChapters.html|Machine Learning]] by Tom Mitchell, McGraw Hill.
* [[http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml|Video lectures]]
* [[http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&discussionID=134161359&gid=961087|Textbooks on machine learning]]
* [[http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf|A Few Useful Things to Know about Machine Learning]] by Pedro Domingos ({{a_few_useful_things_to_know_about_machine_learning.pdf|local copy}})
* [[http://www.lulu.com/product/ebook/clever-algorithms-nature-inspired-programming-recipes/17386095|Clever Algorithms: Nature-Inspired Programming Recipes]] by Jason Brownlee. A handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence.
* [[http://aihub.net/exclusive-machine-learning-methods-and-algorithms-debategraph-map/|Machine Learning Methods and Algorithms Debategraph]]
* [[http://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html|The 10 Algorithms Machine Learning Engineers Need to Know]]
* [[youtube>user/tkorting/videos|Thales Sehn Korting lectures on machine learning]]
* [[http://i.stanford.edu/~ullman/mmds.html?goback=.gde_115439_member_140346964|Mining of Massive Datasets]] by Anand Rajaraman and Jeff Ullman
* [[habrahabr>228989|Обзор наиболее интересных материалов по анализу данных и машинному обучению №4]]
* [[habrahabr>234459|Обзор наиболее интересных материалов по анализу данных и машинному обучению №10 (18 — 25 августа 2014)]]
* [[habrahabr>244579|Обзор наиболее интересных материалов по анализу данных и машинному обучению №24 (24 — 30 ноября 2014)]]
* [[http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&discussionID=106086321&gid=961087|How I can distinguish if I have over-fitting problem or Covariance shift?]]
* [[https://www.kaggle.com/|Machine learning challenges for education, research, and industry]]
* [[habrahabr>276479|Подборка: Более 70 источников по машинному обучению для начинающих]]
* [[http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial|Unsupervised Feature Learning and Deep Learning Tutorial]]
* [[youtube>ZmNOAtZIgIk|Unsupervised Feature Learning and Deep Learning]]
===== Data mining =====
* [[http://www.cs.umd.edu/~samir/498/10Algorithms-08.pdf|Top 10 algorithms in data mining [2007]]] by Xindong Wu, Vipin Kumar -- the presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006
* [[habrahabr>262155|Топ-10 data mining-алгоритмов простым языком]]
* [[http://christonard.com/12-free-data-mining-books/|12 Free (as in beer) Data Mining Books]]
* [[http://textanddatamining.blogspot.com/|Text & Data Mining by practical means]]
* [[http://www.linkedin.com/groups/Please-suggest-best-way-do-115439.S.53131014|Best way to do a keyword-based text sentiment analysis on reviews and topic extraction]]
* [[habrahabr>267827|Big Data vs Data Mining]]
* [[habrahabr>268893|Освоение специальности Data Science на Coursera: личный опыт]]
* [[habrahabr>233763|Автоматическое определение пола по имени]]
* [[habrahabr>254707|Машинное обучение в навигационных устройствах: определяем маневры машины по акселерометру и гироскопу]]
* [[habrahabr>262271|Простой метод для извлечения соотношений и фактов из текста]]
===== Clustering =====
* [[http://machaon.karanagai.com/validation_algorithms.html|Cluster validity algorithms]]
* [[http://dataclustering.cse.msu.edu/publications.html#ensemble|Research in Data Clustering]]
* [[http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html|Self-Tuning Spectral Clustering]]
* [[http://pubs.amstat.org/doi/abs/10.1198/016214503000000666|Finding the Number of Clusters in a Dataset]]
* [[http://www.linkedin.com/groups/Clustering-Vs-Classification-115439.S.101413406|Clustering v.s. Classification]]
===== Neural networks =====
* [[habrahabr>276355|Байесовская нейронная сеть — потому что а почему бы и нет, чёрт возьми (часть 1)]]
* [[http://www.jeffheaton.com/|Books on neural network and bot framework for Java and C#]] by Jeff Heaton
* {{introduction_to_neural_networks_for_java.pdf|Introduction to Neural Networks for Java, 2nd Edition [2012]}} by Jeff Heaton
* [[http://www.linkedin.com/groupItem?view=&srchtype=discussedNews&gid=961087&item=136234391&type=member|Theory of neural networks]]
The best way to understand neural networks is to stop thinking of them as intelligent, but rather as function approximators. So when you give a neural network //n// inputs and expect //m// outputs, you are really just trying to approximate some unknown function //f(Rn) -> Rm//.
Now, the nice thing about real world functions is that they tend not to be **too** pathological. They tend to be at least mostly continuous, they vary more or less smoothly from input to input, etc. And if this is true, the function tends to have a 'nice' approximation.
The underlying assumption of neural networks is that if I take a 'random' function, and sort of push it towards my ideal function enough times in enough places, it will eventually converge to my ideal function. And with real world functions being mostly smooth (plus some noise term), this process is very likely to work in real world situations. What you refer to as 'yielding intelligence' is really just the slow process of the original randomness being replaced by something more closely resembling the ideal function.
I will comment that natural intelligences do appear to store data in very logical ways; the human brain for instance has many highly specialized areas. So, as with human brains, highly trained neural networks are not likely to store data chaotically, but rather in a highly ordered way whose ordering happened to be defined by more or less random events.
Taylor White
I think you are asking how do the ANN perform the approximation and what is happening within the neural networks to yield the result? If I'm right, then I might have an initial response for you. In one of my course on machine learning, our teacher explained the NN's with an analogy to SVM kernels. The idea of the NN is to transform the data points into a space that will have better separation properties. Each layer of connections is a new transformation that will improve the separation properties of the space. That is how you can approximation the separation function that Taylor spoke about. The next phrases are mine own interpretation, but I'm far from being an expert in NN, so feel free to refute it :) : The more layer's you have, the better the transformation MAY be, however the more chances of overlearning you have!
Cédric Penet
Basically, it is multiple parallel interconnected logistic regression. So we have a non-linear equation with a large number of variables that, as was said above, is a universal function approximator, it maps inputs to outputs, based on training examples learned during the supervised training.
Now the biological neural network, that is a very different thing. It is far more complex, with over 35 neurotransmitter chemicals, and much that "modern science" just does not understand.
Brad Morantz PhD
* [[habrahabr>245051|Руководство хакера по нейронным сетям. Схемы реальных значений. Стратегия №3: Аналитический градиент]] (читать по ссылкам)
* [[habrahabr>333772|Копируем человеческий мозг: операция «Свертка»]]
* [[habrahabr>333862|Что читать о нейросетях]]
===== Conferences & courses =====
* [[http://www.ml-class.org/|Online machine learning course]] by Andrew Ng (Stanford)
* [[http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning|Additional lectures]] which are missing in 2011 course.
* [[http://lucy24.bitbucket.org/andrew-ng-about-deep-learning-at-paris-ml-meetup.html|Andrew Ng about Deep Learning at Paris ML Meetup]] ([[youtube>DBD6pKY7kFo#t=2082|direct link to presentation]]).
* [[http://work.caltech.edu/telecourse|Machine Learning course]] recorded at a live broadcast from Caltech
* [[http://www.cikm2011.org/schedule|20th ACM Conference on Information and Knowledge Management (CIKM) 2011 in Glasgow, UK]] (£620 + £140 -- conference + workshop)
* [[http://www.ii-sdv.com/|International Information Conference on Search, Data Mining and Visualization]]
* [[habrahabr>260795|Хакатон по глубинному обучению (deep learning) в Москве]]
* [[https://www.udacity.com/course/deep-learning--ud730|Taking machine learning to the next level]]
* [[https://www.reddit.com/r/learnprogramming/comments/43dvma/best_online_courses_you_took/|Best Online Courses You Took]]
===== Tools & Libraries =====
* [[http://shark-project.sourceforge.net/|Shark Machine Learning Library]] -- C++ library with support for regression and classification tasks (including leaner method, neural networks, kernel methods, [[wp>support vector machines]], ...), solving discrete and continuous optimization problems, multi objective optimization evolutionary algorithms.
* [[wp>WEKA]] -- a collection of visualization tools and algorithms for data analysis and predictive modelling written in Java.
* [[http://project.carrot2.org/index.html|Carrot2]] -- Open Source Search Results Clustering Engine.
* [[http://mahout.apache.org/|Apache Mahout]] -- Apache project to produce free implementations of distributed machine learning algorithms on the [[wp>Hadoop]] platform.
* [[wp>Mallet (software project)|MALLET]] -- a Java-based package for statistical natural language processing, document classification, clustering, topic modelling, information extraction, and other...
* [[http://java-ml.sourceforge.net/|Java-ML]] -- a collection of machine learning algorithms (clustering, classification, cross validation, ...)
* [[http://opencv.org|OpenCV]] includes image processing, video analysis, feature detection, statistical models, bayes classifier, SVM, decision trees, neural networks, clustering, ...
* [[http://genie.sis.pitt.edu/|SMILE]] is a fully portable library of C++ classes implementing graphical decision-theoretic methods, such as Bayesian networks and influence diagrams.
* [[http://jwork.org/main/?q=node/1|Knowledge Java software list]]
* [[http://textanalytics.wikidot.com/tools|Text Processing Tools]]
* [[http://www.linkedin.com/groups/Best-Scalable-commercial-purpose-NLP-115439.S.140367637|Best Scalable commercial purpose NLP framework/Toolkits Poll]]
* [[http://www.linkedin.com/groups/Text-Analytics-Software-Application-115439.S.129294187|Text Analytics Software Applications]]
* [[http://www.linkedin.com/groups/semantic-analyser-tool-115439.S.135088170|Semantic Analyser Tools]]
* [[http://www.kdnuggets.com/2016/08/top-machine-learning-projects-julia.html|Top Machine Learning Projects for]] [[http://julialang.org/|Julia]] -- a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments.
{{tag>machine_learning big_data data_mining}}