82. logical deduction as search: Russell & Norvig (2021, sects. 9.3, 9.4), Poole, Mackworth &
Goebel (1998, pp. ~46–52), Luger & Stubblefield (2004, pp. 62–73), Nilsson (1998, chpt.
4.2, 7.2)
83. Resolution and unification: Russell & Norvig (2021, sections 7.5.2, 9.2, 9.5)
84. Warren, D.H.; Pereira, L.M.; Pereira, F. (1977). "Prolog-the language and its implementation
compared with Lisp". ACM SIGPLAN Notices. 12 (8): 109–115. doi:10.1145/872734.806939
(https://doi.org/10.1145%2F872734.806939).
85. Fuzzy logic: Russell & Norvig (2021, pp. 214, 255, 459), Scientific American (1999)
86. Stochastic methods for uncertain reasoning: Russell & Norvig (2021, chpt. 12–18, 20),
Poole, Mackworth & Goebel (1998, pp. 345–395), Luger & Stubblefield (2004, pp. 165–191,
333–381), Nilsson (1998, chpt. 19)
87. decision theory and decision analysis: Russell & Norvig (2021, chpt. 16–18), Poole,
Mackworth & Goebel (1998, pp. 381–394)
88. Information value theory: Russell & Norvig (2021, sect. 16.6)
89. Markov decision processes and dynamic decision networks: Russell & Norvig (2021, chpt.
17)
90. Stochastic temporal models: Russell & Norvig (2021, chpt. 14) Hidden Markov model:
Russell & Norvig (2021, sect. 14.3) Kalman filters: Russell & Norvig (2021, sect. 14.4)
Dynamic Bayesian networks: Russell & Norvig (2021, sect. 14.5)
91. Game theory and mechanism design: Russell & Norvig (2021, chpt. 18)
92. Bayesian networks: Russell & Norvig (2021, sects. 12.5–12.6, 13.4–13.5, 14.3–14.5, 16.5,
20.2–20.3), Poole, Mackworth & Goebel (1998, pp. 361–381), Luger & Stubblefield (2004,
pp. ~182–190, ≈363–379), Nilsson (1998, chpt. 19.3–19.4)
93. Domingos (2015), chpt. 6.
94. Bayesian inference algorithm: Russell & Norvig (2021, sect. 13.3–13.5), Poole, Mackworth
& Goebel (1998, pp. 361–381), Luger & Stubblefield (2004, pp. ~363–379), Nilsson (1998,
chpt. 19.4 & 7)
95. Domingos (2015), p. 210.
96. Bayesian learning and the expectation–maximization algorithm: Russell & Norvig (2021,
chpt. 20), Poole, Mackworth & Goebel (1998, pp. 424–433), Nilsson (1998, chpt. 20),
Domingos (2015, p. 210)
97. Bayesian decision theory and Bayesian decision networks: Russell & Norvig (2021, sect.
16.5)
98. Statistical learning methods and classifiers: Russell & Norvig (2021, chpt. 20),
99. Ciaramella, Alberto; Ciaramella, Marco (2024). Introduction to Artificial Intelligence: from
data analysis to generative AI. Intellisemantic Editions. ISBN 978-8-8947-8760-3.
100. Decision trees: Russell & Norvig (2021, sect. 19.3), Domingos (2015, p. 88)
101. Non-parameteric learning models such as K-nearest neighbor and support vector machines:
Russell & Norvig (2021, sect. 19.7), Domingos (2015, p. 187) (k-nearest neighbor)
Domingos (2015, p. 88) (kernel methods)
102. Domingos (2015), p. 152.
103. Naive Bayes classifier: Russell & Norvig (2021, sect. 12.6), Domingos (2015, p. 152)
104. Neural networks: Russell & Norvig (2021, chpt. 21), Domingos (2015, Chapter 4)
105. Gradient calculation in computational graphs, backpropagation, automatic differentiation:
Russell & Norvig (2021, sect. 21.2), Luger & Stubblefield (2004, pp. 467–474), Nilsson
(1998, chpt. 3.3)
106. Universal approximation theorem: Russell & Norvig (2021, p. 752) The theorem: Cybenko
(1988), Hornik, Stinchcombe & White (1989)
107. Feedforward neural networks: Russell & Norvig (2021, sect. 21.1)