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              [html]<thead><tr><td>83. Resolution and unification : Russell & Norvig [ 202 ], sections 7.5. 2.9. 2.9. 5 ]</td><td>84. Warren, D. H., Pereeina, L. M., Pereeina, F. ( LST7 ). Prolog - the language and its implementation compared with Lisp * ACM SSGPLAV Motices 12 ( II ): 109 – 1LIS dot $ 33.1. 45 ( RFP2734 -, 80939 ( Mbp : Ultik. org * 21.1. 45 % 29972734.89939 )</td></tr></thead><tbody><tr><td>85. Fuzzy logic : Russell & Norvig ( 2021, pp. 214.255.459 ). Scientific American ( 1999 )</td><td>86. Stochastic methods for uncertain reasoning Rucsell & Norvig ( 2022, chpt. 12 – 13.20 ). Pysie, Mackworth & Gvebel ( 1393, pp. 345 – 395 ), Luger & Studiefield ( 2004, pp. 165 – 131.332 – 382 ). Neisson ( 1298, othpt 129 )</td></tr><tr><td>87. decision theory and decision analysis : Russell & Nonvig ( 2022, chpt. 16 – 18 ), Poske. Mackworth & Govbel [ 12935, pp. 301 – 304 ]</td><td>80. Information value theory : Russell & Nonvig ( 2021, sect. 16.6 )</td></tr><tr><td>89. Markov decision processes and dynamic decision networks : Russell & Norvia ( 2022, chpt. 17 )</td><td>90. Stochaedic temporal models. Russell & Nonvig QS21, chp1.24 ) iden Markov model : Rycose9 & Nonvig ( 2021, sect 1.14.31 Kalman filters ; Russell & Nonyinj ( 2022, sect 14.4 ) Dynamic Bolyesian networks : Russell & Nong ( 2022, sect 14.4 ) Dy</td></tr><tr><td>91. Game theory and mechanism design : Russell & Norvig ( 2021, chpt. 18 )</td><td>92. Bayesian networks : Rucseil & Nonig ( 2022, secto. 125 – 125.23.4 – 125.14 – 13 – 34.5, 16.5, 20.2 – 20.3 ), Poole, Mackworff 8. Goebel ( 12998, pp. 301 – 301 ), Luger & Stublefield ( 2004, pp. - 182 – 130 ), + 290, + 379 ), Niteson ( 1298, other ) ( 12</td></tr><tr><td>93. Domingos ( 2015 ), chpt. 6.</td><td>94 Bayesian infreeze algorithm : Rucsell & Norvig ( 2022, sect. 123 – 23.5 ). Poole, Maskworth & Govebel ( 1999, poru - 301a ). Loger & Studoled ( 2004, pp. – 203 – 279 ). Nitson : ( 399, chpl. 254 & 7 )</td></tr><tr><td>95. Domingos ( 2015 ), p. 210.</td><td>98. Bayessian learning and the expectation – maximization algorithm : Russell & Norvig ( 2023, chtpi, 20 ), pole, Mackworth : & Govebel : ( 1399, pp : 424 – 420 ), hiltesson : ( 1599, chttly, Domingoe, G0125, p. 21 )</td></tr><tr><td>97. Bayesian decision theory and Bayesian decision networks : Russell & Norvia ( 2022, sect. 16.5 )</td><td>98. Statistical learning methods and classifiers : Russell & Norvig ( 2021, chpt. 20 ).</td></tr><tr><td>99. Claramella, Alberto ; Ciaramella, Macro ( 2024 ), Introduction to Artificial Interligence : from data analysis to generative AII intellesemantic Editons. SSIN 578 – 8947 – 9760 – 3.</td><td>100. Decision trees : Russsell & Nonvig ( 2021, sect. 19.3 ), Domingos ( 2015, p. 08 )</td></tr><tr><td>101. Non - parameteric learning models such as K - nearest neighbor and support uector machines : Russell & Noning C022, sect. 1517, Osmingos ( 2015, p. 207 ) ( k - neisest neighbor )</td><td>• Domingos ( 2015, p. 88 ) ( kernel methods )</td></tr><tr><td>102. Domingos ( 2015 ), p. 152.</td><td>103. Naive Bayes classifier. Russell & Norvig ( 2023, sect. 12.6 ). Domingos ( 2015, p. 152 )</td></tr><tr><td>104. Neural networks : Russsell & Nonvig ( 2021, chpt. 21 ), Domingos ( 2015, Chapter 4 )</td><td>120. Gradient calculation in computational graphs, backgropagation, automatic differentiation : Russe9 & Nonig ( 2021. sect. 22 ). Loger 8. Chubbefield ( 2004, pp. 467 – 474 ). Nifesson ( 2398, chpt. 2.13 )</td></tr><tr><td>LIRS Universal approximation theorem : Russell & Nonvig ( 2021, p. 752 ) The theorem : Cybenko ( 2.988 ), Homik, Slanchcombe & White ( 2.999 )</td><td>107. Feedforward neural networks : Russell & Norvig ( 2021, sect. 21.1 )</td></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr><tr></tr></tbody>
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