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* [[Planning]]
* [[Reinforcement Learning]]
* [[Supervised Learning]]
* [[Temporal Difference Learning]]
<span id="Programs"></span>
* [[Bruce Abramson]] ('''1988'''). ''Learning Expected-Outcome Evaluators in Chess.'' Proceedings of the 1988 AAAI Spring Symposium Series: Computer Game Playing, 26-28.
* [[Richard Sutton]] ('''1988'''). ''Learning to Predict by the Methods of Temporal Differences''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Vol. 3, No. 1, [https://webdocs.cs.ualberta.ca/~sutton/papers/sutton-88-with-erratum.pdf pdf]
* [[David E. Goldberg]], [[Mathematician#Holland|John H. Holland]] ('''1988'''). ''[httphttps://wwwlink.springerlinkspringer.com/contentarticle/rw3572714v41q50710.1023/ A:1022602019183 Genetic Algorithms and Machine Learning]''. [https://enwww.wikipediaspringer.orgcom/wikijournal/Machine_Learning_%28journal%29 10994 Machine Learning], Vol. 3
* [[Mathematician#KADeJong|Kenneth A. De Jong]], [[Mathematician#ACSchultz|Alan C. Schultz]] ('''1988'''). ''Using Experience-Based Learning in Game Playing''. Proceedings of the Fifth International Machine Learning Conference, [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.5381 CiteSeerX] » [[Othello]]
* [[Kai-Fu Lee]], [[Sanjoy Mahajan]] ('''1988'''). ''[http://www.sciencedirect.com/science/article/pii/0004370288900768 A Pattern Classification Approach to Evaluation Function Learning]''. [https://en.wikipedia.org/wiki/Artificial_Intelligence_%28journal%29 Artificial Intelligence], Vol. 36, No. 1
* [[Paul E. Utgoff]] ('''1988'''). ''[http://dl.acm.org/citation.cfm?id=896712 ID5: An incremental ID3]''. [http://dblp.uni-trier.de/db/conf/icml/ml1988.html#Utgoff88 ML 1988]
'''1989'''
* [[David E. Goldberg]] ('''1989'''). ''Genetic Algorithms in Search, Optimization and Machine Learning''. [https://en.wikipedia.org/wiki/Addison-Wesley Addison-Wesley]
* [[Robert Levinson]] ('''1989'''). ''A Self-Learning, Pattern-Oriented Chess Program''. [[ICGA Journal#12_4|ICCA Journal, Vol. 12, No. 4]]
* [[Bruce Abramson]] ('''1989'''). ''On Learning and Testing Evaluation Functions.'' Proceedings of the Sixth Israeli Conference on Artificial Intelligence, 1989, 7-16.
* [[Martin Možina]] ('''2009'''). ''Argument Based Machine Learning'', PhD Thesis, [http://www.ailab.si/martin/mozina_phd.pdf pdf]
* [[David Silver]] ('''2009'''). ''Reinforcement Learning and Simulation-Based Search''. Ph.D. thesis, [[University of Alberta]]. [http://webdocs.cs.ualberta.ca/~silver/David_Silver/Publications_files/thesis.pdf pdf]
* [[Eli David|Omid David]], [[Jaap van den Herik]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2009'''). ''Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions''. [[ACM]] Genetic and Evolutionary Computation Conference ([http://www.sigevo.org/gecco-2009/ GECCO '09]), pp. 1483 - 1489, [https://en.wikipediaarxiv.org/wikiabs/Montreal Montreal], Canada, [http1711.06840 arXiv://www.omiddavid.com/pubs/gm-simul1711.pdf pdf06840]
* [[Eli David|Omid David]] ('''2009'''). ''Genetic Algorithms Based Learning for Evolving Intelligent Organisms''. Ph.D. Thesis <ref>[[Dap Hartmann]] ('''2010'''). ''Mimicking the Black Box - Genetically evolving evaluation functions and search algorithms''. Review on Omid David's Ph.D. Thesis, [[ICGA Journal#33_1|ICGA Journal, Vol 33, No. 1]]</ref>
* [[Nur Merve Amil]], [[Nicolas Bredèche]], [[Christian Gagné]], [[Sylvain Gelly]], [[Marc Schoenauer]], [[Olivier Teytaud]] ('''2009'''). ''A Statistical Learning Perspective of Genetic Programming''. EuroGP 2009, [http://hal.inria.fr/docs/00/36/97/82/PDF/eurogp.pdf pdf]
* [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 ...==
* [[Johannes Fürnkranz]], [https://de.wikipedia.org/wiki/Eyke_H%C3%BCllermeier Eyke Hüllermeier] (eds.) ('''2010'''). ''[https://link.springer.com/book/10.1007/978-3-642-14125-6 Preference Learning]''. [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [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]
* [[Eli David|Omid David]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2010'''). ''[http://www.springerlink.com/content/3346t8432n718821 Expert-Driven Genetic Algorithms for Simulating Evaluation Functions]''. [http://www.omiddavid.com/pubs/expert-driven.pdf pdf]
* [[Eli David|Omid David]], [[Nathan S. Netanyahu]], Yoav Rosenberg, Moshe Shimoni ('''2010'''). ''Genetic Algorithms for Automatic Classification of Moving Objects''. [[ACM]] Genetic and Evolutionary Computation Conference ([http://www.sigevo.org/gecco-2010/ GECCO '10]), [https://en.wikipedia.org/wiki/Portland,_Oregon Portland, OR], [http://www.omiddavid.com/pubs/object-classification.pdf pdf]
* [[Eli David|Omid David]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2010'''). ''Genetic Algorithms for Automatic Search Tuning''. [[ICGA Journal#33_2|ICGA Journal, Vol . 33, No. 2]]
* [[Mesut Kirci]] ('''2010'''). ''Feature Learning using State Differences''. Master's thesis, Department of Computing Science, [[University of Alberta]], [http://repository.library.ualberta.ca/dspace/bitstream/10048/1011/1/kirci_mesut_spring+2010.pdf pdf] » [[General Game Playing]]
* [[Amine Bourki]], [[Matthieu Coulm]], [[Philippe Rolet]], [[Olivier Teytaud]], [[Paul Vayssière]] ('''2010'''). ''[http://hal.inria.fr/inria-00467796/en/ Parameter Tuning by Simple Regret Algorithms and Multiple Simultaneous Hypothesis Testing]''. [http://hal.inria.fr/docs/00/46/77/96/PDF/tosubmit.pdf pdf]
* [[Marcin Szubert]], [[Wojciech Jaśkowski]], [[Krzysztof Krawiec]] ('''2011'''). ''Learning Board Evaluation Function for Othello by Hybridizing Coevolution with Temporal Difference Learning''. [http://control.ibspan.waw.pl:3000/mainpage Control and Cybernetics], Vol. 40, No. 3, [http://www.cs.put.poznan.pl/wjaskowski/pub/papers/szubert2011learning.pdf pdf] » [[Othello]]
* [[Hamid Reza Maei]] ('''2011'''). ''Gradient Temporal-Difference Learning Algorithms''. Ph.D. thesis, [[University of Alberta]], advisor [[Richard Sutton]], [http://webdocs.cs.ualberta.ca/~sutton/papers/maei-thesis-2011.pdf pdf]
* [[Eli David|Omid David]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2011'''). ''[https://link.springer.com/article/10.1007/s10710-010-9103-4 Expert-Driven Genetic Algorithms for Simulating Evaluation Functions]''. [https://www.springer.com/journal/10710 Genetic Programming and Evolvable Machines], Vol. 12, No. 1, [https://arxiv.org/abs/1711.06841 arXiv:1711.06841]
'''2012'''
* [[Marco Wiering]], [http://martijnvanotterlo.nl/ Martijn Van Otterlo] ('''2012'''). ''Reinforcement learning: State-of-the-art''. [http://link.springer.com/book/10.1007/978-3-642-27645-3 Adaptation, Learning, and Optimization, Vol. 12], [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]], [[Alex Graves]], [[Ioannis Antonoglou]], [[Daan Wierstra]], [[Martin Riedmiller]] ('''2013'''). ''Playing Atari with Deep Reinforcement Learning''. [http://arxiv.org/abs/1312.5602 arXiv:1312.5602] <ref>[http://www.nervanasys.com/demystifying-deep-reinforcement-learning/ Demystifying Deep Reinforcement Learning] by [http://www.nervanasys.com/author/tambet/ Tambet Matiisen], [http://www.nervanasys.com/ Nervana], December 21, 2015</ref> <ref>[http://www.google.com/patents/US20150100530 Patent US20150100530 - Methods and apparatus for reinforcement learning - Google Patents]</ref>
'''2014'''
* [[Eli David|Omid E. David]], [[Jaap van den Herik]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2014'''). ''Genetic Algorithms for Evolving Computer Chess Programs''. [[IEEE#EC|IEEE Transactions on Evolutionary Computation]], [httphttps://www.genetic-programmingarxiv.org/hc2014abs/David-Paper1711.pdf pdf] <ref>[http08337 arXiv://www1711.liacs.nl/nieuws/jaap-van-den-herik-wint-humies-award-2014/ Jaap van den Herik wint Humies Award 2014 - LIACS - Leiden Institute of Advanced Computer Science08337]</ref>
* [[Wojciech Jaśkowski]], [[Marcin Szubert]], [[Paweł Liskowski]] ('''2014'''). ''Multi-Criteria Comparison of Coevolution and Temporal Difference Learning on Othello''. [http://www.evostar.org/2014/ EvoApplications 2014], [http://www.springer.com/computer/theoretical+computer+science/book/978-3-662-45522-7 Springer, volume 8602] » [[Othello]]
* [[Marcin Szubert]], [[Wojciech Jaśkowski]] ('''2014'''). ''Temporal Difference Learning of N-Tuple Networks for the Game 2048''. [[IEEE#CIG|IEEE Conference on Computational Intelligence and Games]], [http://www.cs.put.poznan.pl/mszubert/pub/szubert2014cig.pdf pdf] <ref>[https://en.wikipedia.org/wiki/2048_%28video_game%29 2048 (video game) from Wikipedia]</ref>
* [[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=
* [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.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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