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=Learning Paradigms=
There are three major learning [https://en.wikipedia.org/wiki/Paradigm paradigms], each corresponding to a particular abstract learning task. These are [https://en.wikipedia.org/wiki/Supervised_learning [Supervised Learning|supervised learning]], [https://en.wikipedia.org/wiki/Unsupervised_learning unsupervised learning] and [[Reinforcement Learning|reinforcement learning]]. Usually any given type of [[Neural Networks|neural network]] architecture can be employed in any of those tasks.
==Supervised Learning==
''see main page [[Supervised Learning]]''
 
Supervised learning is learning from examples provided by a knowledgable external supervisor. In machine learning, supervised learning is a technique for deducing a function from training data. The training data consist of pairs of input objects and desired outputs, f.i. in computer chess a sequence of positions associated with the outcome of a game <ref>[http://www.aihorizon.com/essays/generalai/supervised_unsupervised_machine_learning.htm AI Horizon: Machine Learning, Part II: Supervised and Unsupervised Learning]</ref> .
* [[Planning]]
* [[Reinforcement Learning]]
* [[Supervised Learning]]
* [[Temporal Difference Learning]]
<span id="Programs"></span>
=Programs=
* [[Allie]]
* [[AlphaZero]]
* [[Alexs]]
* [[Mark Levene]], [[Trevor Fenner]] ('''2009'''). ''A Methodology for Learning Players' Styles from Game Records''. [http://arxiv.org/abs/0904.2595v1 arXiv:0904.2595v1]
* [[Mathematician#THastie|Trevor Hastie]], [[Mathematician#RTibshirani|Robert Tibshirani]], [https://en.wikipedia.org/wiki/Jerome_H._Friedman Jerome Friedman] ('''2009'''). ''[http://www.springer.com/book/9780387848570 The Elements of Statistical Learning: Data Mining, Inference, and Prediction]''. Second Edition, [https://de.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [https://dblp.uni-trier.de/pers/hd/h/Hall:Mark_A= Mark A. Hall], [https://dblp.uni-trier.de/pers/hd/f/Frank:Eibe Eibe Frank], [[Geoffrey Holmes]], [[Bernhard Pfahringer]], [https://dblp.uni-trier.de/pers/hd/r/Reutemann:Peter Peter Reutemann], [[Ian H. Witten]] ('''2009'''). ''The WEKA data mining software: an update''. [https://dblp.uni-trier.de/db/journals/sigkdd/sigkdd11.html SIGKDD Explorations], Vol. 11, No. 1, [https://www.kdd.org/exploration_files/p2V11n1.pdf pdf] <ref>[https://en.wikipedia.org/wiki/Weka_(machine_learning) Weka (machine learning) from Wikipedia]</ref>
==2010 ...==
* [https://dblp.uni-trier.de/pers/hd/f/Frank:Eibe Eibe Frank], [https://dblp.uni-trier.de/pers/hd/h/Hall:Mark_A= Mark A. Hall], [[Geoffrey Holmes]], [https://dblp.uni-trier.de/pers/hd/k/Kirkby:Richard Richard Kirkby], [[Bernhard Pfahringer]], [[Ian H. Witten]], [https://dblp.uni-trier.de/pers/hd/t/Trigg:Leonard_E= Len Trigg] ('''2010'''). ''[https://link.springer.com/chapter/10.1007/978-0-387-09823-4_66 Weka-A Machine Learning Workbench for Data Mining]''. [https://link.springer.com/book/10.1007/978-0-387-09823-4 Data Mining and Knowledge Discovery Handbook], [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [[Jacek Mańdziuk]] ('''2010'''). ''[http://link.springer.com/book/10.1007%2F978-3-642-11678-0 Knowledge-Free and Learning-Based Methods in Intelligent Game Playing]''. [http://link.springer.com/bookseries/7092 Studies in Computational Intelligence], Vol. 276, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [[Joel Veness]], [[Kee Siong Ng]], [[Marcus Hutter]], [[David Silver]] ('''2010'''). ''Reinforcement Learning via AIXI Approximation''. Association for the Advancement of Artificial Intelligence (AAAI), [http://jveness.info/publications/veness_rl_via_aixi_approx.pdf pdf]
* [[Takeshi Ito]] ('''2018'''). ''Game learning support system based on future position''. [[CG 2018]], [[ICGA Journal#40_4|ICGA Journal, Vol. 40, No. 4]]
'''2019'''
* [[Herilalaina Rakotoarison]], [[Marc Schoenauer]], [[Michèle Sebag]] ('''2019'''). ''Automated Machine Learning with Monte-Carlo Tree Search''. [https://arxiv.org/abs/1906.00170 arXiv:1906.00170]
* [[Frank Hutter]], [https://dblp.org/pers/hd/k/Kotthoff:Lars Lars Kotthoff], [https://dblp.org/pers/hd/v/Vanschoren:Joaquin Joaquin Vanschoren] (eds.) ('''2019'''). ''[https://link.springer.com/book/10.1007%2F978-3-030-05318-5 Automated Machine Learning]''. [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2019'''). ''Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model''. [https://arxiv.org/abs/1911.08265 arXiv:1911.08265] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=72381 New DeepMind paper] by GregNeto, [[CCC]], November 21, 2019</ref>
=Forum Posts=
* [http://www.talkchess.com/forum/viewtopic.php?t=56313 Position learning and opening books] by Forrest Hoch, [[CCC]], May 11, 2015
* [http://www.talkchess.com/forum/viewtopic.php?t=61861 A database for learning evaluation functions] by [[Álvaro Begué]], [[CCC]], October 28, 2016 » [[Automated Tuning]], [[Evaluation]], [[Texel's Tuning Method]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=72020 A book on machine learning] by Mehdi Amini, [[CCC]], October 06, 2019
=External Links=
* [https://en.wikipedia.org/wiki/Data_mining Data mining from Wikipeadia]
* [https://en.wikipedia.org/wiki/Ensemble_learning Ensemble learning from Wikipedia]
** [https://en.wikipedia.org/wiki/Bootstrap_aggregating Bootstrap aggregating from Wikipedia]
* [https://en.wikipedia.org/wiki/Explanation-based_learning Explanation-based learning from Wikipedia]
* [https://en.wikipedia.org/wiki/Meta_learning_%28computer_science%29 Meta Learning from Wikipedia]
* [http://www.aihorizon.com/essays/generalai/no_free_lunch_machine_learning.htm AI Horizon: Machine Learning, Part III: Testing Algorithms, and The "No Free Lunch Theorem"]
==Chess==
* [http://www.top-5000.nl/authors/rebel/hints.htm Learning Methods] by [[Ed Schroder|Ed Schröder]]
* [http://archive.ics.uci.edu/ml/datasets/Chess+%28King-Rook+vs.+King-Pawn%29 UCI Machine Learning Repository: Chess (King-Rook vs. King-Pawn) Data Set] by [[Alen Shapiro]]
* [https://en.chessbase.com/post/standing-on-the-shoulders-of-giants Standing on the shoulders of giants] by [[Albert Silver]], [[ChessBase|ChessBase News]], September 18, 2019
==Supervised Learning==
* [https://en.wikipedia.org/wiki/Supervised_learning Supervised learning from Wikipedia]
* [http://www.scholarpedia.org/article/Category:Supervised_learning Category: Supervised learning - Scholarpedia]
* [https://en.wikipedia.org/wiki/Boosting_%28machine_learning%29 Boosting (machine learning) from Wikipedia]
: ** [https://en.wikipedia.org/wiki/AdaBoost AdaBoost from Wikipedia]
* [https://en.wikipedia.org/wiki/Computational_learning_theory Computational learning theory from Wikipedia]
* [https://en.wikipedia.org/wiki/Support_vector_machine Support vector machine from Wikipedia]
* [https://en.wikipedia.org/wiki/Statistical_learning_theory Statistical learning theory from Wikipedia]
* [https://en.wikipedia.org/wiki/Statistical_classification Statistical classification from Wikipedia]
: ** [https://en.wikipedia.org/wiki/Naive_Bayes_classifier Naive Bayes classifier from Wikipedia]: ** [https://en.wikipedia.org/wiki/Probabilistic_classification Probabilistic classification from Wikipedia]
* [https://en.wikipedia.org/wiki/Statistical_mechanics Statistical mechanics from Wikipedia]
* [https://en.wikipedia.org/wiki/Bayesian_network Bayesian network from Wikipedia]
* [https://en.wikipedia.org/wiki/Mean_squared_error Mean squared error from Wikipedia]
* [https://en.wikipedia.org/wiki/Regression_analysis Regression analysis from Wikipedia]
: ** [https://en.wikipedia.org/wiki/Outline_of_regression_analysis Outline of regression analysis from Wikipedia]: ** [https://en.wikipedia.org/wiki/Linear_regression Linear regression from Wikipedia]: ** [https://en.wikipedia.org/wiki/Logistic_regression Logistic regression from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability Probability from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability_theory Probability theory from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability_density_function Probability density function from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability_distribution Probability distribution from Wikipedia]
: ** [https://en.wikipedia.org/wiki/Normal_distribution Normal distribution from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability_measure Probability measure from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability_space Probability space from Wikipedia]
* [https://en.wikipedia.org/wiki/Pseudorandomness Pseudorandomness from Wikipedia]
: ** [https://en.wikipedia.org/wiki/Pseudorandom_number_generator Pseudorandom number generator from Wikipedia]: ** [https://en.wikipedia.org/wiki/Pseudo-random_number_sampling Pseudo-random number sampling from Wikipedia]
* [https://en.wikipedia.org/wiki/Randomness Randomness from Wikipedia]
: ** [https://en.wikipedia.org/wiki/Statistical_randomness Statistical randomness from Wikipedia]
* [https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_theory Vapnik–Chervonenkis theory from Wikipedia]
* [https://en.wikipedia.org/wiki/VC_dimension VC dimension from Wikipedia]
* [http://www.scholarpedia.org/article/Hopfield_network Hopfield network - Scholarpedia]
* [https://en.wikipedia.org/wiki/Long_short_term_memory Long short term memory from Wikipedia]
'''Blogs'''
* [https://theneural.wordpress.com/ Neural Networks Blog] by [[Ilya Sutskever]]
* [http://dynamicnotions.blogspot.com/ Dynamic Notions] by [http://www.blogger.com/profile/07894297206547597169 John Wakefield] , a Blog about the evolution of neural networks with [[C sharp|C#]] samples:
: [http://dynamicnotions.blogspot.com/2008/09/single-layer-perceptron.html The Single Layer Perceptron]
: [http://dynamicnotions.blogspot.com/2008/09/hidden-neurons-and-feature-space.html Hidden Neurons and Feature Space]
: [http://dynamicnotions.blogspot.com/2008/09/training-neural-networks-using-back.html Training Neural Networks Using Back Propagation in C#]
: [http://dynamicnotions.blogspot.com/2008/09/data-mining-with-artificial-neural.html Data Mining with Artificial Neural Networks (ANN)]
* [http://www.welchlabs.com/blog Blog - Welch Labs]
==Courses==
* [http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html Advanced Topics: RL] by [[David Silver]]
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