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Learning, the process of acquiring new knowledge which involves synthesizing different types of information. Machine learning as aspect of computer chess programming deals with algorithms that allow the program to change its behavior based on data, which for instance occurs during game playing against a variety of opponents considering the final outcome and/or the game record for instance as history score chart indexed by ply. Related to Machine learning is evolutionary computation and its sub-areas of genetic algorithms, and genetic programming, that mimics the process of natural evolution, as further mentioned in automated tuning. The process of learning often implies understanding, perception or reasoning. So called Rote learning avoids understanding and focuses on memorization. Inductive learning takes examples and generalizes rather than starting with existing knowledge. Deductive learning takes abstract concepts to make sense of examples.

=Learning inside a Chess Program= Learning inside a chess program may address several disjoint issues. A persistent hash table remembers "important" positions from earlier games inside the search with its exact score. Worse positions may be avoided in advance. Learning opening book moves, that is appending successful novelties or modify the probability of already stored moves from the book based on the outcome of a game. Another application is learning evaluation weights of various features, f. i. piece- or piece-square values or mobility. Programs may also learn to control search or time usage.

=Learning Paradigms= There are three major learning paradigms, each corresponding to a particular abstract learning task. These are supervised learning, unsupervised learning and reinforcement learning. Usually any given type of neural network architecture can be employed in any of those tasks.

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.

Unsupervised Learning
Unsupervised machine learning seems much harder: the goal is to have the computer learn how to do something that we don't tell it how to do. The learner is given only unlabeled examples, f. i. a sequence of positions of a running game but the final result (still) unknown. A form of reinforcement learning can be used for unsupervised learning, where an agent bases its actions on the previous rewards and punishments without necessarily even learning any information about the exact ways that its actions affect the world. Clustering is another method of unsupervised learning.

Reinforcement Learning
see main page Reinforcement Learning

Reinforcement learning is defined not by characterizing learning methods, but by characterizing a learning problem. Reinforcement learning is learning what to do - how to map situations to actions - so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them. The reinforcement learning problem is deeply indebted to the idea of Markov decision processes (MDPs) from the field of optimal control.

=Learning Topics= =Programs=
 * Automated Tuning
 * Bayesian Networks
 * Book Learning
 * CHREST
 * Deep Learning
 * EPAM
 * Genetic Programming
 * Neural Networks
 * Pattern Learning
 * Pattern Recognition
 * Persistent Hash Table
 * Planning
 * Reinforcement Learning
 * Temporal Difference Learning
 * AlphaZero
 * Alexs
 * Bebe
 * Blondie25
 * ChessMaps
 * Chessterfield
 * CHUMP
 * Deep Pink
 * Falcon
 * Giraffe
 * Golch
 * KnightCap
 * LCZero
 * Meep
 * Morph
 * NeuroChess
 * RomiChess
 * Octavius
 * SAL
 * Stoofvlees
 * TDChess
 * Tempo
 * Winter

=See also=
 * Cognition
 * Dynamic Programming
 * Knowledge
 * Memory
 * Psychology
 * Robots
 * Trial and Error

=Selected Publications=

1940 ...

 * Walter Pitts (1942). Some observations on the simple neuron circuit. Bulletin of Mathematical Biology, Vol. 4, No. 3
 * Warren S. McCulloch, Walter Pitts (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biology, Vol. 5, No. 1
 * Donald O. Hebb (1949). The Organization of Behavior. Wiley & Sons

1950 ...

 * Stephen C. Kleene (1951) Representation of Events in Nerve Nets and Finite Automata. RM-704, RAND paper, pdf, reprinted in
 * Claude Shannon, John McCarthy (eds.) (1956). Automata Studies. Annals of Mathematics Studies, No. 34


 * Paul I. Richards (1951). Machines which can learn. American Scientist, 39:711-716
 * Paul I. Richards (1952). On Game Learning Machines. The Scientific Monthly, Vol. 74, No. 4, April 1952
 * Alan Turing (1953). Chess. part of the collection Digital Computers Applied to Games in Bertram Vivian Bowden (editor), Faster Than Thought, a symposium on digital computing machines, reprinted 1988 in Computer Chess Compendium, reprinted in
 * Alan Turing, Jack Copeland (editor) (2004). The Essential Turing, Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus The Secrets of Enigma. Oxford University Press, amazon, google books


 * Marvin Minsky (1954). Neural Nets and the Brain Model Problem. Ph.D. dissertation, Princeton University

1955 ...

 * John von Neumann (1956). Probabilistic Logic and the Synthesis of Reliable Organisms From Unreliable Components. in
 * Claude Shannon, John McCarthy (eds.) (1956). Automata Studies. Annals of Mathematics Studies, No. 34, pdf


 * Frank Rosenblatt (1957). The Perceptron - a Perceiving and Recognizing Automaton. Report 85-460-1, Cornell Aeronautical Laboratory
 * Albert M. Uttley (1959). Imitation of Pattern Recognition and Trial-and-error Learning in a Conditional Probability Computer. Reviews of Modern Physics, Vol. 31, April 1959, pp. 546-548
 * Arthur Samuel (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal July 1959 » Checkers
 * Edward Feigenbaum (1959). An Information Processing Theory of Verbal Learning. RAND Paper

1960 ...

 * Edward Feigenbaum (1960). Information Theories of Human Verbal Learning. Ph.D. thesis, Carnegie Mellon University, advisor Herbert Simon
 * Edward Feigenbaum (1961). The Simulation of Verbal Learning Behavior. Proceedings Western Joint Conference, Vol. 19
 * Edward Feigenbaum, Herbert Simon (1961). Performance of a Reading Task by an Elementary Perceiving and Memorizing Program. RAND Paper, pdf
 * Donald Michie (1961). Trial and Error. Penguin Science Survey, pdf
 * Edward Feigenbaum, Herbert Simon (1962). A Theory of the Serial Position Effect. British Journal of Psychology, Vol. 53, 307-32, pdf
 * Earl B. Hunt (1962). Concept Learning: An Information Processing Problem. Wiley. google books
 * Frank Rosenblatt (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books
 * Allen Newell (1963). Learning, Generality and Problem Solving. Memorandum RM-3285-1-PR pdf
 * Herbert Simon, Edward Feigenbaum (1964). An Information-processing Theory of Some Effects of Similarity, Familiarization, and Meaningfulness in Verbal Learning. Journal of Verbal Learning and Verbal Behavior, Vol. 3, No. 5, pdf

1965 ...

 * James R. Slagle (1965). A multipurpose Theorem Proving Heuristic Program that learns. IFIP Congress 65, Vol. 2
 * Donald Michie (1966). Game Playing and Game Learning Automata. Advances in Programming and Non-Numerical Computation, Leslie Fox (ed.), pp. 183-200. Oxford, Pergamon. » Includes Appendix: Rules of SOMAC by John Maynard Smith, introduces Expectiminimax tree
 * Thomas A. Throop (1966). Thoughts on the Development of Computer Learning Programs. Defense Technical Information Center
 * Arnold K. Griffith (1966). A new Machine-Learning Technique applied to the Game of Checkers. MIT, Project MAC, MAC-M-293
 * Arthur Samuel (1967). Some Studies in Machine Learning. Using the Game of Checkers. II-Recent Progress. pdf
 * Marvin Minsky, Seymour Papert (1969). Perceptrons.

1970 ...

 * Albert Zobrist (1970). A Pattern Recognition Program which uses a Geometry-Preserving Representation of Features. Technical Report #85, pdf
 * Vladimir Vapnik, Alexey Chervonenkis (1971). On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities. Theory of Probability and its Applications, Vol. 16, No. 2
 * A. Harry Klopf (1972). Brain Function and Adaptive Systems - A Heterostatic Theory. Air Force Cambridge Research Laboratories, Special Reports, No. 133, pdf
 * Marvin Minsky, Seymour Papert (1972). Perceptrons: An Introduction to Computational Geometry. The MIT Press, 2nd edition with corrections
 * Herbert Simon, Kevin J. Gilmartin (1973). A Simulation of Memory for Chess Positions. Cognitive Psychology, Vol. 5, pp. 29-46. pdf
 * Arnold K. Griffith (1974). A Comparison and Evaluation of Three Machine Learning Procedures as Applied to the Game of Checkers. Artificial Intelligence, Vol. 5, No. 2 » Checkers

1975 ...

 * Jacques Pitrat (1976). A Program to Learn to Play Chess. Pattern Recognition and Artificial Intelligence, pp. 399-419. Academic Press Ltd. London, UK. ISBN 0-12-170950-7.
 * Jacques Pitrat (1976). Realization of a Program Learning to Find Combinations at Chess. Computer Oriented Learning Processes (ed. J. Simon). Noordhoff, Groningen, The Netherlands.
 * Pericles Negri (1977). Inductive Learning in a Hierarchical Model for Representing Knowledge in Chess End Games. pdf
 * Ryszard Michalski, Pericles Negri (1977). An experiment on inductive learning in chess endgames. Machine Intelligence 8, pdf
 * Boris Stilman (1977). The Computer Learns. in 1976 US Computer Chess Championship, by David Levy, Computer Science Press, Woodland Hills, CA, pp. 83-90
 * Richard Sutton (1978). Single channel theory: A neuronal theory of learning. Brain Theory Newsletter 3, No. 3/4, pp. 72-75.
 * Ross Quinlan (1979). Discovering Rules by Induction from Large Collections of Examples. Expert Systems in the Micro-electronic Age, pp. 168-201. Edinburgh University Press (Introducing ID3)

1980 ...

 * Sarah E. Goldin, Philip Klahr (1981). Learning and Abstraction in Simulation. IJCAI 1981, pdf
 * Paul E. Utgoff, Tom Mitchell (1982). Acquisition of Appropriate Bias for Inductive Concept Learning. AAAI 1982, pdf
 * A. Harry Klopf (1982). The Hedonistic Neuron: A Theory of Memory, Learning, and Intelligence. Hemisphere Publishing Corporation, University of Michigan
 * Alen Shapiro, Tim Niblett (1982). Automatic Induction of Classification Rules for Chess End game. Advances in Computer Chess 3
 * Thomas Nitsche (1982). A Learning Chess Program. Advances in Computer Chess 3
 * Ryszard Michalski, Jaime Carbonell, Tom Mitchell (1983). Machine Learning: An Artificial Intelligence Approach. Tioga Publishing Company, ISBN 0-935382-05-4. google books
 * Ross Quinlan (1983). Learning efficient classification procedures and their application to chess end games. In Machine Learning: An Artificial Intelligence Approach, pages 463–482. Tioga, Palo Alto
 * Alen Shapiro (1983). The Role of Structured Induction in Expert Systems. University of Edinburgh, Machine Intelligence Research Unit (Ph.D. thesis)
 * Edward Feigenbaum, Herbert Simon (1984). EPAMlike models of recognition and learning. Cognitive Science, Vol. 8, 305-336, pdf
 * John E. Laird, Paul S. Rosenbloom, Allen Newell (1984). Towards Chunking as a General Learning Mechanism. AAAI 1984
 * Albrecht Heeffer (1984). Automated Acquisition on Concepts for the Description of Middle-game Positions in Chess. Turing Institute, Glasgow, Scotland, TIRM-84-005
 * Paul E. Utgoff (1984). Shift of Bias for Inductive Concept Learning. Ph.D. thesis, Rutgers University, New Brunswick
 * Leslie Valiant (1984). A Theory of the Learnable. Communications of the ACM, Vol. 27, No. 11, pdf

1985 ...
1986 1987 1988 1989
 * Tony Marsland (1985). Evaluation-Function Factors. ICCA Journal, Vol. 8, No. 2, pdf
 * Albrecht Heeffer (1985). Validating Concepts from Automated Acquisition Systems. IJCAI 85, pdf
 * Hans Berliner (1985). Goals, Plans, and Mechanisms: Non-symbolically in an Evaluation Surface. Presentation at Evolution, Games, and Learning, Center for Nonlinear Studies, Los Alamos National Laboratory, May 21.
 * Ryszard Michalski, Jaime Carbonell, Tom Mitchell (1985). Machine Learning: An Artificial Intelligence Approach. Morgan Kaufmann, ISBN 0-934613-09-5. google books
 * Steven Skiena (1986). An Overview of Machine Learning in Chess. ICCA Journal, Vol. 9, No. 1
 * Jens Christensen, Richard Korf (1986). A Unified Theory of Heuristic Evaluation functions and Its Applications to Learning. Proceedings of the AAAI-86, pp. 148-152, pdf.
 * Ryszard Michalski, Jaime Carbonell, Tom Mitchell (1986). Machine Learning: An Artificial Intelligence Approach, Volume II. Morgan Kaufmann, ISBN 0-934613-00-1. google books
 * Tom Mitchell, Jaime Carbonell, Ryszard Michalski (1986). Machine Learning: A Guide to Current Research. The Kluwer International Series in Engineering and Computer Science, Vol. 12
 * Ivan Bratko, Igor Kononenko (1986). Learning Rules from Incomplete and Noisy Data. Proceedings Unicom Seminar on the Scope of Artificial Intelligence in Statistics. Technical Press
 * David Slate (1987). A Chess Program that uses its Transposition Table to Learn from Experience. ICCA Journal, Vol. 10, No. 2
 * Ronald L. Rivest (1987). Learning Decision Lists. Machine Learning 2,3, pdf 2001
 * Gerald Tesauro, Terrence J. Sejnowski (1987). A 'Neural' Network that Learns to Play Backgammon. NIPS 1987
 * Alen Shapiro (1987). Structured Induction in Expert Systems. Turing Institute Press in association with Addison-Wesley Publishing Company, Workingham, UK
 * Alberto Maria Segre (1987). On the Operationality/Generality Trade-off in Explanation-based Learning. IJCAI 1987, pdf
 * Alberto Maria Segre (1987). Explanation-Based Learning of Generalized Robot Assembly Plans. Ph.D. thesis, University of Illinois at Urbana-Champaign, Advisor: Gerald Francis DeJong, II
 * Eric B. Baum, Frank Wilczek (1987). Supervised Learning of Probability Distributions by Neural Networks. NIPS 1987
 * 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. Machine Learning, Vol. 3, No. 1, pdf
 * David E. Goldberg, John H. Holland (1988). Genetic Algorithms and Machine Learning. Machine Learning, Vol. 3
 * Kenneth A. De Jong, Alan C. Schultz (1988). Using Experience-Based Learning in Game Playing. Proceedings of the Fifth International Machine Learning Conference, CiteSeerX » Othello
 * Kai-Fu Lee, Sanjoy Mahajan (1988). A Pattern Classification Approach to Evaluation Function Learning. Artificial Intelligence, Vol. 36, No. 1
 * Paul E. Utgoff (1988). ID5: An incremental ID3. ML 1988
 * Robert Levinson (1989). A Self-Learning, Pattern-Oriented Chess Program. 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.
 * Eric Wefald, Stuart Russell (1989). Adaptive Learning of Decision-Theoretic Search Control Knowledge. In Proceedings of the Sixth International Workshop on Machine Learning. Ithaca, NY: Morgan Kaufmann
 * Stephen Muggleton, Michael Bain, Jean Hayes Michie, Donald Michie (1989). An Experimental Comparison of Human and Machine Learning Formalisms. 6. ML 1989, pdf
 * Eric B. Baum (1989). A Proposal for More Powerful Learning Algorithms. Neural Computation, Vol. 1, No. 2
 * Susan L. Epstein (1989). The Intelligent Novice - Learning to Play Better. Heuristic Programming in Artificial Intelligence 1
 * Chris Watkins (1989). Learning from Delayed Rewards. Ph.D. thesis, Cambridge University, pdf

1990 ...
1991 1992 1993 1994
 * Richard Sutton, Andrew Barto (1990). Time Derivative Models of Pavlovian Reinforcement. Learning and Computational Neuroscience: Foundations of Adaptive Networks: 497-537.
 * Bruce Abramson (1990). On Learning and Testing Evaluation Functions. Journal of Experimental and Theoretical Artificial Intelligence 2: 241-251.
 * Tony Scherzer, Linda Scherzer, Dean Tjaden (1990). Learning in Bebe. Computers, Chess, and Cognition » Mephisto Best-Publication Award
 * Yves Kodratoff, Ryszard Michalski (1990). Machine Learning: An Artificial Intelligence Approach, Volume III. Morgan Kaufmann, ISBN 1-55860-119-8. google books
 * Michèle Sebag (1990). A symbolic-numerical approach for supervised learning from examples and rules. Ph.D. thesis, Paris Dauphine University
 * Robert Schapire (1991). The Design and Analysis of Efficient Learning Algorithms. Ph.D. thesis, Massachusetts Institute of Technology, supervisor Ronald L. Rivest, pdf
 * Gerhard Mehlsam, Hermann Kaindl, Wilhelm Barth (1991). Feature Construction During Tree Learning. GWAI 1991: 50-61.
 * Alex van Tiggelen (1991). Neural Networks as a Guide to Optimization - The Chess Middle Game Explored. ICCA Journal, Vol. 14, No. 3
 * William Tunstall-Pedoe (1991). Genetic Algorithms Optimizing Evaluation Functions. ICCA Journal, Vol. 14, No. 3
 * Tony Scherzer, Linda Scherzer, Dean Tjaden (1991). Learning in Bebe. ICCA Journal, Vol. 14, No. 4
 * Steven Walczak (1991). Predicting Actions from Induction on Past Performance. Proceedings of the 8th International Workshop on Machine Learning, pp. 275-279. Morgan Kaufmann
 * Paul E. Utgoff, Jeffery A. Clouse (1991). Two Kinds of Training Information for Evaluation Function Learning. University of Massachusetts, Amherst, Proceedings of the AAAI 1991
 * Byoung-Tak Zhang, Gerd Veenker (1991). Neural networks that teach themselves through genetic discovery of novel examples. IEEE International Joint Conference on Neural Networks
 * Miroslav Kubat (1992). Introduction to Machine Learning. Advanced Topics in Artificial Intelligence 1992
 * Michael Bain (1992). Learning optimal chess strategies. Proc. Intl. Workshop on Inductive Logic Programming (ed. Stephen Muggleton), Institute for New Generation Computer Technology, Tokyo, Japan.
 * Eduardo F. Morales (1992). First-Order Induction of Patterns in Chess. Ph.D. Thesis, The Turing Institute, University of Strathclyde, Glasgow
 * Eduardo F. Morales (1992). Learning Chess Patterns. Inductive Logic Programming (ed. Stephen Muggleton), Academic Press, The Apic Series, London, UK
 * Gerald Tesauro (1992). Temporal Difference Learning of Backgammon Strategy. ML 1992
 * Chris Watkins, Peter Dayan (1992). Q-learning. Machine Learning, Vol. 8, No. 2
 * Gerald Tesauro (1992). Practical Issues in Temporal Difference Learning. Machine Learning, Vol. 8, No. 3-4
 * Manuela Veloso (1992). Learning by Analogical Reasoning in General Purpose Problem Solving. Ph.D. thesis, Carnegie Mellon University, advisor Jaime Carbonell
 * Michael Gherrity (1993). A Game Learning Machine. Ph.D. Thesis, University of California, San Diego, zipped ps
 * Shaul Markovitch, Yaron Sella (1993). Learning of Resource Allocation Strategies for Game Playing, The proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France. pdf
 * David Carmel, Shaul Markovitch (1993). Learning Models of Opponent's Strategy in Game Playing. AAAI Proceedings, CiteSeerX
 * Dan Geiger, Azaria Paz, Judea Pearl (1993). Learning simple causal structures. International Journal of Intelligent Systems, 8, pp. 231-247.
 * Sebastian Thrun, Tom Mitchell (1993). Integrating Inductive Neural Network Learning and Explanation-Based Learning. IJCAI 1993, zipped ps
 * Alois Heinz, Christoph Hense (1993). Bootstrap learning of α-β-evaluation functions. ICCI 1993, pdf
 * Eduardo F. Morales (1994). Learning Patterns for Playing Strategies. ICCA Journal, Vol. 17, No. 1
 * Fernand Gobet, Peter Jansen (1994). Towards a chess program based on a model of human memory. Advances in Computer Chess 7 » CHUMP
 * Michael Bain (1994). Learning Logical Exceptions in Chess. Ph.D. thesis, University of Strathclyde, CitySeerX
 * Michael Bain, Stephen Muggleton (1994). Learning Optimal Chess Strategies. Machine Intelligence 13 (eds. K. Furukawa and Donald Michie), pp. 291-309. Oxford University Press, Oxford, UK. ISBN 0198538502.
 * Ryszard Michalski, George Tecuci (1994). Machine Learning: A Multistrategy Approach, Volume IV. Morgan Kaufmann, ISBN 1-55860-251-8. google books
 * Gerald Tesauro (1994). TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play. Neural Computation Vol. 6, No. 2
 * Alberto Maria Segre, Charles Elkan (1994). A High-Performance Explanation-Based Learning Algorithm. Artificial Intelligence, Vol. 68, Nos. 1-2
 * David E. Moriarty, Risto Miikkulainen (1994). Evolving Neural Networks to focus Minimax Search. AAAI-94, pdf
 * Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski (1994). Temporal Difference Learning of Position Evaluation in the Game of Go. Advances in Neural Information Processing Systems 6

1995 ...
1996 1997 1998
 * Gerhard Mehlsam, Hermann Kaindl, Wilhelm Barth (1995). Feature Construction during Tree Learning. GOSLER Final Report 1995: 391-403
 * Chris McConnell (1995). Tuning Evaluation Functions for Search. ps or pdf from CiteSeerX
 * David Heckerman, Dan Geiger, Max Chickering (1995). Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Machine Learning, Vol. 20, pdf
 * Tristan Cazenave (1995). Learning and Problem Solving in Gogol, a Go playing program. pdf
 * Gerald Tesauro (1995). Temporal Difference Learning and TD-Gammon. Communications of the ACM Vol. 38, No. 3
 * Sebastian Thrun (1995). Learning to Play the Game of Chess. in Gerald Tesauro, David S. Touretzky, Todd K. Leen (eds.) Advances in Neural Information Processing Systems 7, MIT Press
 * Marco Wiering (1995). TD Learning of Game Evaluation Functions with Hierarchical Neural Architectures. Master's thesis, University of Amsterdam, pdf
 * Michael A. Arbib (ed.) (1995, 2002). The Handbook of Brain Theory and Neural Networks. The MIT Press
 * Nicol N. Schraudolph (1995). Optimization of Entropy with Neural Networks. Ph.D. thesis, University of California, San Diego
 * Robert W. Howard (1995). Learning and Memory: Major Ideas, Principles, Issues and Applications. Praeger, amazon.com
 * Leemon C. Baird III, Mance E. Harmon, A. Harry Klopf (1996). Reinforcement Learning: An Alternative Approach to Machine Intelligence. pdf
 * Sebastian Thrun (1996). Explanation-Based Neural Network Learning: A Lifelong Learning Approach. Kluwer Academic Publishers
 * Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore (1996). Reinforcement Learning: A Survey. JAIR Vol. 4, pdf
 * Eduardo F. Morales (1996). Learning Playing Strategies in Chess. Computational Intelligence, Vol. 12, No. 1, CiteSeerX
 * Wee Sun Lee (1996). Agnostic Learning and Single Hidden Layer Neural Networks. Ph.D. thesis, Australian National University, ps
 * Johannes Fürnkranz (1996). Machine Learning in Computer Chess: The Next Generation. ICCA Journal, Vol. 19, No. 3, zipped ps
 * Adriaan de Groot, Fernand Gobet (1996). Perception and memory in chess. Heuristics of the professional eye. Assen: Van Gorcum, The Netherlands. ISBN 90-232-2949-5. Chapter 9; A discussion: Two authors, two different views? word
 * Stuart Russell (1996). Machine Learning. Chapter 4 of M. A. Boden (Ed.), Artificial Intelligence, Academic Press. Part of the Handbook of Perception and Cognition, ps
 * Barney Pell, Susan L. Epstein, Robert Levinson (1996). Introduction to the special issue on games: Structure and Learning. Computational Intelligence, Vol. 12, No. 1, pdf
 * Robert Levinson (1996). General Game-Playing and Reinforcement Learning. Computational Intelligence, Vol. 12, No. 1
 * Tristan Cazenave (1996). Learning to forecast by explaining the consequences of actions. pdf
 * Tristan Cazenave (1996). Self fuzzy learning. pdf
 * Yoav Freund, Robert Schapire (1996). Game Theory, On-line Prediction and Boosting. COLT 1996, pdf
 * Yoav Freund, Robert Schapire (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, Vol. 55, No. 1, 1996 pdf » AdaBoost
 * Sepp Hochreiter, Jürgen Schmidhuber (1997). Long short-term memory. Neural Computation, Vol. 9, No. 8, pdf
 * Eduardo F. Morales (1997). On Learning How to Play. Advances in Computer Chess 8, CiteSeerX
 * Don Beal, Martin C. Smith (1997). Learning Piece Values Using Temporal Differences. ICCA Journal, Vol. 20, No. 3
 * Kieran Greer, Piyush Ojha, David A. Bell (1997). Learning Search Heuristics from Examples: A Study in Computer Chess, Seventh Conference of the Spanish Association for Artificial Intelligence, CAEPIA’97, November, pp. 695-704.
 * Nir Friedman, Moises Goldszmidt, David Heckerman, Stuart Russell (1997). Where is the Impact of Bayesian Networks in Learning? In Proc. Fifteenth International Joint Conference on Artificial Intelligence, Nagoya, Japan, ps
 * Ronald Parr, Stuart Russell (1997). Reinforcement Learning with Hierarchies of Machines. In Advances in Neural Information Processing Systems 10, MIT Press, zipped ps
 * Tristan Cazenave (1997). Gogol (an Analytical Learning Program). IJCAI'97, pdf
 * Tom Mitchell (1997). Machine Learning. McGraw Hill
 * Michèle Sebag (1997). Stochastic Heuristics for Machine Learning & Machine Learning for Stochastic Optimization. Habilitation, Paris-Sud 11 University
 * William Uther, Manuela M. Veloso (1997). Adversarial Reinforcement Learning. Carnegie Mellon University, ps
 * William Uther, Manuela M. Veloso (1997). Generalizing Adversarial Reinforcement Learning. Carnegie Mellon University, ps
 * Marco Wiering, Jürgen Schmidhuber (1997). HQ-learning. Adaptive Behavior, Vol. 6, No 2
 * Jonathan Baxter, Andrew Tridgell, Lex Weaver (1998). Knightcap: A chess program that learns by combining td(λ) with game-tree search, Proceedings of the 15th International Conference on Machine Learning, pdf via citeseerX
 * Jonathan Baxter, Andrew Tridgell, Lex Weaver (1998). TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search. Australian Journal of Intelligent Information Processing Systems, Vol. 5 No. 1, arXiv:cs/9901001
 * Jonathan Baxter, Andrew Tridgell, Lex Weaver (1998). Experiments in Parameter Learning Using Temporal Differences. ICCA Journal, Volume 21 No. 2, pdf
 * Lev Finkelstein, Shaul Markovitch (1998). Learning to Play Chess Selectively by Acquiring Move Patterns. ICCA Journal, Vol. 21, No. 2, pdf
 * Csaba Szepesvári (1998). Reinforcement Learning: Theory and Practice. Proceedings of the 2nd Slovak Conference on Artificial Neural Networks, zipped ps
 * Richard Sutton, Andrew Barto (1998). Reinforcement Learning: An Introduction. MIT Press
 * Ryszard Michalski, Ivan Bratko, Miroslav Kubat (eds.) (1998). Machine Learning and Data Mining: Methods and Applications. John Wiley & Sons
 * Miroslav Kubat, Ivan Bratko, Ryszard Michalski (1998). A Review of Machine Learning Methods. pdf

1999
 * Nobusuke Sasaki, Yasuji Sawada, Jin Yoshimura (1998). A Neural Network Program of Tsume-Go. CG 1998
 * Tristan Cazenave (1998). Machine Introspection for Machine Learning. Tucson 1998, pdf
 * Tristan Cazenave (1998). Integration of Different Reasoning Modes in a Go Playing and Learning System. pdf
 * Tristan Cazenave (1998). Learning with Fuzzy Definitions of Goals. pdf
 * Ryszard Michalski (1998). Learnable Evolution: Combining Symbolic and Evolutionary Learning. Proceedings of the Fourth International Workshop on Multistrategy Learning (MSL'98)
 * Krzysztof Krawiec, Roman Slowinski, Irmina Szczesniak (1998). Pedagogical Method for Extraction of Symbolic Knowledge from Neural Networks. Rough Sets and Current Trends in Computing 1998
 * Marco Wiering, Jürgen Schmidhuber (1998). Fast online Q (λ). Machine Learning, Vol. 33, No. 1
 * Robert Hyatt (1999). Book Learning - a Methodology to Tune an Opening Book Automatically. ICCA Journal, Vol. 22, No. 1
 * Kieran Greer, Piyush Ojha, David A. Bell (1999). A Pattern-Oriented Approach to Move Ordering: the Chessmaps Heuristic. ICCA Journal, Vol. 22, No. 1
 * Michael Buro (1999). Toward Opening Book Learning. ICCA Journal, Vol. 22, No. 2, pdf
 * Don Beal, Martin C. Smith (1999). Learning Piece-Square Values using Temporal Differences. ICCA Journal, Vol. 22, No. 4
 * David Heckerman (1999). A tutorial on learning with Bayesian networks. pdf from CiteSeerX
 * F. De Comité, F. Denis, R. Gilleron et Fabien Letouzey (1999). Positive and Unlabeled Examples help Learning, The 10th International Conference on Algorithmic Learning Theory, ps
 * Vassilis Papavassiliou, Stuart Russell (1999). Convergence of reinforcement learning with general function approximators. In Proc. IJCAI-99, Stockholm, ps
 * Philip G. K. Reiser, Patricia J. Riddle (1999). Evolving Logic Programs to Classify Chess-Endgame Positions. Simulated Evolution and Learning, Canberra, Australia. Lecture Notes in Artificial Intelligence, No. 1585, Springer, pdf » Endgame
 * Marco Wiering (1999). Explorations in Efficient Reinforcement Learning. Ph.D. thesis, University of Amsterdam, advisors Frans Groen and Jürgen Schmidhuber
 * Geoffrey E. Hinton, Terrence J. Sejnowski (eds.) (1999). Unsupervised Learning: Foundations of Neural Computation. MIT Press

2000 ...
2001 2002 2003 2004
 * Miroslav Kubat, Jan Žižka (2000). Learning Middle Game Patterns in Chess: A Case Study. Lecture Notes in Computer Science, Vol. 1821, Springer
 * Vladimir Vapnik (2000). The nature of statistical learning theory. Springer
 * Sebastian Thrun, Michael L. Littman (2000). A Review of Reinforcement Learning. AI Magazine, Vol. 21, No. 1
 * Johannes Fürnkranz (2000). Machine Learning in Games: A Survey. Austrian Research Institute for Artificial Intelligence, OEFAI-TR-2000-3, pdf
 * Johannes Fürnkranz, Bernhard Pfahringer, Hermann Kaindl, Stefan Kramer (2000). Learning to Use Operational Advice. ECAI-00, pdf
 * Jack van Rijswijck (2000). Learning from Perfection: A Data Mining Approach to Evaluation Function Learning in Awari. CG 2000, pdf
 * Robert Levinson, Ryan Weber (2000). Chess Neighborhoods, Function Combination, and Reinforcement Learning. CG 2000
 * Jan Ramon, Tom Francis, Hendrik Blockeel (2000). Learning a Go Heuristic with Tilde. CG 2000
 * Levente Kocsis, Jos Uiterwijk, Jaap van den Herik (2000). Learning Time Allocation using Neural Networks. CG 2000, postscript
 * Michael Buro (2000). Toward Opening Book Learning. Games in AI Research (eds. Jaap van den Herik and Hiroyuki Iida), pp. 47-54. Universiteit Maastricht, Maastricht, The Netherlands. ISBN 90-621-6416-1.
 * Fabien Letouzey, François Denis, Rémi Gilleron (2000). Learning from Positive and Unlabeled Examples. ALT 2000: 71-85, ps
 * Andrew Ng, Stuart Russell (2000). Algorithms for inverse reinforcement learning. In Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, California: Morgan Kaufmann, pdf
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2010 ...
2011 2012
 * Jacek Mańdziuk (2010). Knowledge-Free and Learning-Based Methods in Intelligent Game Playing. Studies in Computational Intelligence, Vol. 276, Springer
 * Joel Veness, Kee Siong Ng, Marcus Hutter, David Silver (2010). Reinforcement Learning via AIXI Approximation. Association for the Advancement of Artificial Intelligence (AAAI), pdf
 * Omid David, Moshe Koppel, Nathan S. Netanyahu (2010). Expert-Driven Genetic Algorithms for Simulating Evaluation Functions. pdf
 * Omid David, Nathan S. Netanyahu, Yoav Rosenberg, Moshe Shimoni (2010). Genetic Algorithms for Automatic Classification of Moving Objects. ACM Genetic and Evolutionary Computation Conference (GECCO '10), Portland, OR, pdf
 * Omid David, Moshe Koppel, Nathan S. Netanyahu (2010). Genetic Algorithms for Automatic Search Tuning. ICGA Journal, Vol 33, No. 2
 * Mesut Kirci (2010). Feature Learning using State Differences. Master's thesis, Department of Computing Science, University of Alberta, pdf » General Game Playing
 * Amine Bourki, Matthieu Coulm, Philippe Rolet, Olivier Teytaud, Paul Vayssière (2010). Parameter Tuning by Simple Regret Algorithms and Multiple Simultaneous Hypothesis Testing. pdf
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 * Edward P. Manning (2010). Coevolution in a Large Search Space using Resource-limited Nash Memory. GECCO '10 » Othello
 * Marco Wiering (2010). Self-play and using an expert to learn to play backgammon with temporal difference learning. Journal of Intelligent Learning Systems and Applications, Vol. 2, No. 2
 * Joel Veness (2011). Approximate Universal Artificial Intelligence and Self-Play Learning for Games. Ph.D. thesis, University of New South Wales, supervisors: Kee Siong Ng, Marcus Hutter, Alan Blair, William Uther, John Lloyd; pdf
 * Mesut Kirci, Nathan Sturtevant, Jonathan Schaeffer (2011). A GGP Feature Learning Algorithm. KI 25(1): 35-42, pdf » General Game Playing
 * I-Chen Wu, Hsin-Ti Tsai, Hung-Hsuan Lin, Yi-Shan Lin, Chieh-Min Chang, Ping-Hung Lin (2011). Temporal Difference Learning for Connect6. Advances in Computer Games 13
 * Tomoyuki Kaneko, Kunihito Hoki (2011). Analysis of Evaluation-Function Learning by Comparison of Sibling Nodes. Advances in Computer Games 13
 * Jiao Wang, Shiyuan Li, Jitong Chen, Xin Wei, Huizhan Lv, Xinhe Xu (2011). 4*4-Pattern and Bayesian Learning in Monte-Carlo Go. Advances in Computer Games 13
 * Charles Elkan (2011). Reinforcement Learning with a Bilinear Q Function. EWRL 2011
 * Krzysztof Krawiec, Marcin Szubert (2011). Learning N-Tuple Networks for Othello by Coevolutionary Gradient Search. GECCO 2011, pdf
 * Krzysztof Krawiec, Wojciech Jaśkowski, Marcin Szubert (2011). Evolving small-board Go players using Coevolutionary Temporal Difference Learning with Archives. Applied Mathematics and Computer Science, Vol. 21, No. 4
 * Marcin Szubert, Wojciech Jaśkowski, Krzysztof Krawiec (2011). Learning Board Evaluation Function for Othello by Hybridizing Coevolution with Temporal Difference Learning. Control and Cybernetics, Vol. 40, No. 3, pdf » Othello
 * Hamid Reza Maei (2011). Gradient Temporal-Difference Learning Algorithms. Ph.D. thesis, University of Alberta, advisor Richard Sutton, pdf
 * Marco Wiering, Martijn Van Otterlo (2012). Reinforcement learning: State-of-the-art. Adaptation, Learning, and Optimization, Vol. 12, Springer
 * István Szita (2012). Reinforcement Learning in Games. Chapter 17

2013 2014
 * Sjoerd van den Dries, Marco Wiering (2012). Neural-fitted TD-leaf learning for playing Othello with structured neural networks. IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, No. 11
 * Amir Ban (2012). Automatic Learning of Evaluation, with Applications to Computer Chess. Discussion Paper 613, The Hebrew University of Jerusalem - Center for the Study of Rationality, Givat Ram
 * Adrien Couetoux, Olivier Teytaud, Hassen Doghmen (2012). Learning a Move-Generator for Upper Confidence Trees. ICS 2012, Hualien, Taiwan, December 2012 » UCT
 * Robert Schapire, Yoav Freund (2012). Boosting: Foundations and Algorithms. MIT Press
 * Arthur Guez, David Silver, Peter Dayan (2012). Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search. NIPS 2012, pdf
 * Peter Dayan (2012). How to set the switches on this thing. Current Opinion in Neurobiology, Vol. 22, pdf
 * Arthur Guez, David Silver, Peter Dayan (2013). Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search. Journal of Artificial Intelligence Research, Vol. 48, pdf
 * Katja Grace (2013). Algorithmic Progress in Six Domains. Technical report 2013-3, Machine Intelligence Research Institute, Berkeley, CA, pdf, 5 Game Playing, 5.1 Chess, 5.2 Go, 9 Machine Learning
 * Marcin Szubert, Wojciech Jaśkowski, Paweł Liskowski, Krzysztof Krawiec (2013). Shaping Fitness Function for Evolutionary Learning of Game Strategies. GECCO 2013, pdf
 * Marcin Szubert, Wojciech Jaśkowski, Krzysztof Krawiec (2013). On Scalability, Generalization, and Hybridization of Coevolutionary Learning: a Case Study for Othello. IEEE Transactions on Computational Intelligence and AI in Games, Vol. 5, No. 3 » Othello
 * Michiel van der Ree, Marco Wiering (2013). Reinforcement Learning in the Game of Othello: Learning Against a Fixed Opponent and Learning from Self-Play. ADPRL 2013
 * Luuk Bom, Ruud Henken, Marco Wiering (2013). Reinforcement Learning to Train Ms. Pac-Man Using Higher-order Action-relative Inputs. ADPRL 2013
 * Peter Auer, Marcus Hutter, Laurent Orseau (2013). Reinforcement Learning. Dagstuhl Reports, Vol. 3, No. 8, DOI: 10.4230/DagRep.3.8.1, URN: urn:nbn:de:0030-drops-43409
 * Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller (2013). Playing Atari with Deep Reinforcement Learning. arXiv:1312.5602
 * Omid David, Jaap van den Herik, Moshe Koppel, Nathan S. Netanyahu (2014). Genetic Algorithms for Evolving Computer Chess Programs. IEEE Transactions on Evolutionary Computation, pdf
 * Wojciech Jaśkowski, Marcin Szubert, Paweł Liskowski (2014). Multi-Criteria Comparison of Coevolution and Temporal Difference Learning on Othello. EvoApplications 2014, Springer, volume 8602 » Othello
 * Marcin Szubert, Wojciech Jaśkowski (2014). Temporal Difference Learning of N-Tuple Networks for the Game 2048. IEEE Conference on Computational Intelligence and Games, pdf
 * Marcin Szubert (2014). Coevolutionary Shaping for Reinforcement Learning. Ph.D. thesis, Poznań University of Technology, supervisor Krzysztof Krawiec, co-supervisor Wojciech Jaśkowski, pdf
 * Wojciech Jaśkowski (2014). Systematic n-Tuple Networks for Othello Position Evaluation. ICGA Journal, Vol. 37, No. 2, preprint as pdf » Othello
 * Christopher Clark, Amos Storkey (2014). Teaching Deep Convolutional Neural Networks to Play Go. arXiv:1412.3409 » Neural Networks
 * Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver (2014). Move Evaluation in Go Using Deep Convolutional Neural Networks. arXiv:1412.6564v1
 * I-Chen Wu, Kun-Hao Yeh, Chao-Chin Liang, Chia-Chuan Chang, Han Chiang (2014). Multi-Stage Temporal Difference Learning for 2048. TAAI 2014, best paper award
 * Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos (2014). Regret bounds for restless Markov bandits. Theoretical Computer Science 558, pdf

2015 ...
2016 2017
 * Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis (2015). Human-level control through deep reinforcement learning. Nature, Vol. 518
 * Tobias Graf, Marco Platzner (2015). Adaptive Playouts in Monte Carlo Tree Search with Policy Gradient Reinforcement Learning. Advances in Computer Games 14
 * Yuichiro Sato, Hiroyuki Iida, Jaap van den Herik (2015). Transfer Learning by Inductive Logic Programming. Advances in Computer Games 14
 * Kokolo Ikeda, Takanari Shishido, Simon Viennot (2015). Machine-Learning of Shape Names for the Game of Go. Advances in Computer Games 14
 * Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Veda Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, Shane Legg, Volodymyr Mnih, Koray Kavukcuoglu, David Silver (2015). Massively Parallel Methods for Deep Reinforcement Learning. arXiv:1507.04296
 * Matthew Lai (2015). Giraffe: Using Deep Reinforcement Learning to Play Chess. M.Sc. thesis, Imperial College London, arXiv:1509.01549v1 » Giraffe
 * Hado van Hasselt, Arthur Guez, David Silver (2015). Deep Reinforcement Learning with Double Q-learning. arXiv:1509.06461
 * Tom Schaul, John Quan, Ioannis Antonoglou, David Silver (2015). Prioritized Experience Replay. arXiv:1511.05952
 * Miroslav Kubat (2015). An Introduction to Machine Learning. Springer
 * Christian Wirth, Johannes Fürnkranz (2015). On Learning From Game Annotations. IEEE Transactions on Computational Intelligence and AI in Games, Vol. 7, No. 3
 * Dharshan Kumaran, Demis Hassabis, James L. McClelland (2016). What learning systems do intelligent agents need? Complementary Learning Systems Theory Updated. Trends in Cognitive Sciences, Vol. 20, No. 7, pdf
 * Ziyu Wang, Nando de Freitas, Marc Lanctot (2016). Dueling Network Architectures for Deep Reinforcement Learning. arXiv:1511.06581
 * Jialin Liu, Olivier Teytaud, Tristan Cazenave (2016). Fast seed-learning algorithms for games. CG 2016
 * Omid E. David, Nathan S. Netanyahu, Lior Wolf (2016). DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess. ICAAN 2016, Lecture Notes in Computer Science, Vol. 9887, Springer, pdf preprint » DeepChess
 * Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016). Deep Learning. MIT Press
 * Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu (2016). Reinforcement Learning with Unsupervised Auxiliary Tasks. arXiv:1611.05397v1
 * Muthuraman Chidambaram, Yanjun Qi (2017). Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently. arXiv:1702.06762v1 » Neural Networks
 * Johannes Fürnkranz (2017). Machine Learning and Game Playing. in Claude Sammut, Geoffrey I. Webb (eds) (2017). Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA
 * David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis (2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. arXiv:1712.01815 » AlphaZero

=Forum Posts=

1998 ...

 * Book learning and rating bias by Don Dailey, CCC, May 01, 1998
 * BookLearning Under the Microscope!!! by Robert Henry Durrett, CCC, August 31, 1998
 * Book learning? by Werner Inmann, CCC, December 31, 1998
 * Book learning by James Robertson, CCC, September 12, 1999

2000 ...

 * question about book and learning by Uri Blass, CCC, April 26, 2002
 * Time to implement Learning by Tom Likens, CCC, February 26, 2004

2005 ...

 * RomiChess && learning or the emperor has no clothes by Michael Sherwin, Winboard Programming Forum, May 19, 2006
 * learning by Jim, CCC, February 03, 2008
 * Information on engines with learning capabilities by Martin Thoresen, CCC, April 06, 2008
 * naive bayes classifier by Don Dailey, CCC, July 21, 2009

2010 ...

 * [Computer-go learning patterns for mc go] by Hendrik Baier, Computer Go Archive, April 26, 2010
 * Positional learning by Ben-Hur Carlos Vieira Langoni Junior, CCC, December 13, 2010
 * Ban: Automatic Learning of Evaluation [...] by BB+, OpenChess Forum, May 10, 2012
 * Teaching Deep Convolutional Neural Networks to Play Go by Hiroshi Yamashita, The Computer-go Archives, December 14, 2014
 * Teaching Deep Convolutional Neural Networks to Play Go by Michel Van den Bergh, CCC, December 16, 2014

2015 ...

 * Piece weights with regression analysis (in Russian) by Vladimir Medvedev, CCC, April 30, 2015 » Point Value by Regression Analysis
 * Position learning and opening books by Forrest Hoch, CCC, May 11, 2015
 * A database for learning evaluation functions by Álvaro Begué, CCC, October 28, 2016 » Automated Tuning, Evaluation, Texel's Tuning Method

=External Links=
 * Learning from Wikipedia
 * Classical conditioning from Wikipeadia
 * Learnable Evolution Model from Wikipedia
 * Monkey see Monkey do from Wikipeadia
 * Observational learning from Wikipeadia
 * Rote learning from Wikipeadia

Machine Learning

 * Machine learning from Wikipeadia
 * Machine Learning in Games by Jay Scott
 * Machine Learning by Martin Sewell
 * List of machine learning concepts from Wikipedia
 * Apprenticeship learning from Wikipedia
 * Ensemble learning from Wikipedia
 * Explanation-based learning from Wikipedia
 * Meta Learning from Wikipedia
 * Online Machine Learning from Wikipedia
 * PAC Learning from Wikipedia
 * Learning Robots / Robot Learning by Jürgen Schmidhuber
 * Similarity learning from Wikipedia
 * Universal Learning Machines - Optimal Universal AI by Jürgen Schmidhuber
 * UCI Machine Learning Repository from University of California, Irvine
 * NMCS4All: Machine Learning by David H. Ackley, YouTube Video

AI

 * Artificial Intelligence II by Nikos Drakos, Computer Based Learning Unit, University of Leeds
 * Learning I
 * Learning II


 * AI Horizon: Machine Learning, Part I: Types of Learning Problems
 * AI Horizon: Machine Learning, Part II: Supervised and Unsupervised Learning
 * AI Horizon: Machine Learning, Part III: Testing Algorithms, and The "No Free Lunch Theorem"

Chess

 * Learning Methods by Ed Schröder
 * UCI Machine Learning Repository: Chess (King-Rook vs. King-Pawn) Data Set by Alen Shapiro

Supervised Learning

 * Supervised learning from Wikipedia
 * Category: Supervised learning - Scholarpedia
 * Boosting (machine learning) from Wikipedia
 * AdaBoost from Wikipedia


 * Computational learning theory from Wikipedia
 * Support vector machine from Wikipedia

Unsupervised Learning

 * Unsupervised learning from Wikipedia
 * Category: Unsupervised learning - Scholarpedia

Reinforcement Learning

 * Reinforcement Learning from Wikipeadia
 * Reinforcement Learning: An Introduction ebook by Richard Sutton and Andrew Barto
 * Reinforcement Learning in Classic Board Games (pdf) by David Silver
 * Category: Reinforcement Learning - Scholarpedia
 * Reinforcement Learning - Scholarpedia
 * Reinforcement Learning and POMDPs by Jürgen Schmidhuber
 * Q-learning from Wikipeadia

TD Learning

 * Temporal Difference Learning from Wikipeadia
 * Temporal difference learning - Scholarpedia
 * TD-Gammon from Wikipeadi
 * Td-gammon - Scholarpedia

Statistics

 * Statistics from Wikipedia
 * Statistical learning theory from Wikipedia
 * Statistical classification from Wikipedia
 * Naive Bayes classifier from Wikipedia
 * Probabilistic classification from Wikipedia


 * Statistical mechanics from Wikipedia
 * Bayesian network from Wikipedia
 * Bayesian spam filtering from Wikipedia
 * Can a Bayesian spam filter play chess? by Laird A. Breyer
 * Computational statistics from Wikipedia
 * Cluster analysis from Wikipedia
 * Cross entropy from Wikipedia
 * Dimensionality reduction from Wikipedia
 * Feature extraction from Wikipedia
 * Feature selection from Wikipedia
 * Mean squared error from Wikipedia
 * Regression analysis from Wikipedia
 * Outline of regression analysis from Wikipedia
 * Linear regression from Wikipedia
 * Logistic regression from Wikipedia


 * Probability from Wikipedia
 * Probability theory from Wikipedia
 * Probability density function from Wikipedia
 * Probability distribution from Wikipedia
 * Normal distribution from Wikipedia


 * Probability measure from Wikipedia
 * Probability space from Wikipedia
 * Pseudorandomness from Wikipedia
 * Pseudorandom number generator from Wikipedia
 * Pseudo-random number sampling from Wikipedia


 * Randomness from Wikipedia
 * Statistical randomness from Wikipedia


 * Vapnik–Chervonenkis theory from Wikipedia
 * VC dimension from Wikipedia

Markov Models

 * Markov model from Wikipedia
 * Markov chain from Wikipedia
 * Hidden Markov model from Wikipedia
 * Markov decision process (MDP) from Wikipedia
 * Partially observable Markov decision process (POMDP) from Wikipedia

NNs

 * Introduction to Neural Networks by Nicol N. Schraudolph and Fred Cummins
 * Biological neural network from Wikipedia
 * Category: Neural networks - Scholarpedia
 * Computational neuroscience from Wikipedia
 * Dendrite from Wikipedia
 * Hebbian theory from Wikipedia
 * Generalized Hebbian Algorithm from Wikipedia
 * Long-term potentiation from Wikipedia
 * Neuron from Wikipedia
 * Neural pathway from Wikipedia
 * Neurotransmitter from Wikipedia
 * Synapse from Wikipedia
 * Synaptic plasticity from Wikipedia

ANNs
Topics RNNs Blogs
 * Artificial neural network from Wikipedia
 * Artificial Neural Networks - Wikibooks
 * Chess end games using Neural Networks
 * Artificial neuron from Wikipedia
 * Backpropagation from Wikipedia
 * Connectionism from Wikipedia
 * Convolutional neural network from Wikipedia
 * Feedforward neural network from Wikipedia
 * Fuzzy neural network - Scholarpedia
 * Multilayer perceptron from Wikipedia
 * Neocognitron from Wikipedia
 * Perceptron from Wikipedia
 * Recursive neural network from Wikipedia
 * Rprop from Wikipedia
 * Time delay neural network from Wikipedia
 * Recurrent neural network from Wikipedia
 * Recurrent neural networks - Scholarpedia
 * Recurrent Neural Networks by Jürgen Schmidhuber
 * Boltzmann machine from Wikipedia
 * Deep Learning from Wikipeadia
 * Echo state network
 * Hopfield network from Wikipedia
 * Hopfield network - Scholarpedia
 * Long short term memory from Wikipedia
 * Neural Networks Blog by Ilya Sutskever
 * Dynamic Notions by John Wakefield, a Blog about the evolution of neural networks with C# samples:
 * The Single Layer Perceptron
 * Hidden Neurons and Feature Space
 * Training Neural Networks Using Back Propagation in C#
 * Data Mining with Artificial Neural Networks (ANN)


 * Blog - Welch Labs

Courses

 * Advanced Topics: RL by David Silver
 * Learning From Data - Online Course (MOOC) by Yaser Abu-Mostafa, Caltech
 * Machine Learning and Probabilistic Graphical Models: Course Materials by Sargur Srihari, University at Buffalo
 * Neural Networks Demystified by Stephen Welch, Welch Labs
 * Stanford Machine Learning by Andrew Ng
 * Lecture 1 | Machine Learning (Stanford) by Andrew Ng, YouTube Video

=References=

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