Probability, statistics and origins

Cybernetics, information theory, PAC learning, SVMs, ensembles, Bayesian networks.

Solid lines are confirmed citations (OpenAlex or the citing paper's own reference list); dotted lines are influences stated in textbooks without a confirmed citation. Vertical position is the theme group.

Year	First author	Title	Era
1948	Shannon	A Mathematical Theory of Communication	Ch. 1 Origins 1943–56
1948	Wiener	Cybernetics: or Control and Communication in the Animal and the Machine	Ch. 1 Origins 1943–56
1950	Turing	Computing Machinery and Intelligence	Ch. 1 Origins 1943–56
1961	Minsky	Steps toward Artificial Intelligence	Ch. 2 Symbols and perceptrons 1956–74
1973	Lighthill	Artificial Intelligence: A General Survey	Ch. 2 Symbols and perceptrons 1956–74
1975	Holland	Adaptation in Natural and Artificial Systems	Ch. 3 Knowledge systems and connectionism 1974–93
1975	Newell & Simon	Computer science as empirical inquiry: symbols and search	Ch. 3 Knowledge systems and connectionism 1974–93
1976	Dempster et al.	Maximum Likelihood from Incomplete Data Via the EM Algorithm	Ch. 3 Knowledge systems and connectionism 1974–93
1980	Searle	Minds, brains, and programs	Ch. 3 Knowledge systems and connectionism 1974–93
1984	Geman & Geman	Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images	Ch. 3 Knowledge systems and connectionism 1974–93
1986	Quinlan	Induction of decision trees	Ch. 3 Knowledge systems and connectionism 1974–93
1988	Pearl	Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference	Ch. 3 Knowledge systems and connectionism 1974–93
1989	Schapire	The strength of weak learnability	Ch. 4 Statistical learning 1990–2011
1992	Boser et al.	A training algorithm for optimal margin classifiers	Ch. 4 Statistical learning 1990–2011
1993	Quinlan	C4.5: Programs for Machine Learning	Ch. 4 Statistical learning 1990–2011
1994	Breiman	Bagging predictors	Ch. 4 Statistical learning 1990–2011
1995	Freund & Schapire	A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting	Ch. 4 Statistical learning 1990–2011
1995	Russell & Norvig	Artificial Intelligence: A Modern Approach	Ch. 4 Statistical learning 1990–2011
1995	Cortes & Vapnik	Support-vector networks	Ch. 4 Statistical learning 1990–2011
1996	Tibshirani	Regression Shrinkage and Selection Via the Lasso	Ch. 4 Statistical learning 1990–2011
2001	Breiman	Random Forests	Ch. 4 Statistical learning 1990–2011
2003	Blei et al.	Latent Dirichlet Allocation	Ch. 4 Statistical learning 1990–2011
2014	Schmidhuber	Deep learning in neural networks: An overview	Ch. 5 Deep learning 2012–17
2015	LeCun et al.	Deep learning	Ch. 5 Deep learning 2012–17
2021	Jumper et al.	Highly accurate protein structure prediction with AlphaFold	Ch. 6 Pretraining and generation 2017–22
2021	Bommasani et al.	On the Opportunities and Risks of Foundation Models	Ch. 6 Pretraining and generation 2017–22