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              [html]<thead><tr><td>43. Computational learning theory : Russell & Nonig ( 2021, pp. 672 – 674 ), Jordan & Mitchell ( 2015 )</td><td>50. Natural lamguage processing ( NLP ); Russall & Norvig ( 202L, chpt. 23 – 24 ); Poole, Mackworth & Govebjel ( LSRB, pp. 51 – 31 – 104 ), Luger & Stubblefield ( 2004, pp. 591 – 502 )</td></tr></thead><tbody><tr><td>51. Subproblems of NLP : Russell & Norvig ( 2021, pp. 849 – 850 )</td><td>52. Russell & Norvig ( 2021 ), pp. 856 – 858.</td></tr><tr><td>53. Dickson ( 2022 ).</td><td>54 Modern statistical and deep learning approaches to NLP. Russell & Nonvig ( ZEDL chpt. 24 ). Cambria & White ( 2014 )</td></tr><tr><td>55. Vincent ( 2019 ).</td><td>56. Russell & Norvig ( 2021 ), pp. 875 – 878.</td></tr><tr><td>57. Bushwick ( 2023 ).</td><td>58. Computer vision : Russell & Norvig ( 20 / 21, chpt. 25 ), Nilsson ( 1999, chpt. 6 )</td></tr><tr><td>59. Russell & Norvig ( 2021 ), pp. 849 – 850.</td><td>60. Russell & Norvig ( 2021 ), pp. 895 – 899.</td></tr><tr><td>61. Russell & Norvig ( 2021 ), pp. 899 – 901.</td><td>62. Challa et al. ( 2011 )</td></tr><tr><td>63. Russell & Norvig ( 2021 ), pp. 931 – 938.</td><td>64. MIT AIL ( 2014 ).</td></tr><tr><td>65. Affective computing : Thro ( 1993 ), Edelson ( 1991 ), Tao & Tan ( 2005 ), Scassellati ( 2002 )</td><td>66. Waddell ( 2018 ).</td></tr><tr><td>67. Poria et al. ( 2017 ).</td><td>6B. Search algorithms : Ruzsell & Norvig ( 2021, chpts : 3 – 5 ), Froole, Mackworth & Goebel [ 2980, pp. 127 – 253 ], Lsger. 8_Stubblefield [ 2004, pp. 70 – 354.150 – 213 ], Nelsson ( 1398, chpts. 7 – 22 ]</td></tr><tr><td>69. State space search : Russell & Nonvig ( 2021, chpt. 3 )</td><td>70. Russell & Norvig ( 2021 ), sect. 11.2</td></tr><tr><td>72. Uninformed searches ( breadth first search, deapth - first search and general state space search ); Russsell & Nonya ( 2022, searci. 3.4 ), Protein Machonorth : & Gorebet. ( 1398 ), pp. 12.3 – 122 ), Luger 4 Studeletid ( 2004, pp. 12.4 ), Pra</td><td>72. Heuristic or informed searches ( e. g., greedy best fest and A *). Russell & Norvig ( 2022, sect. 3.5 ). Poole, Mackworth & Gorbei ( 1398, pp. 122 – 247 ). Poole & Mackworth ( 2017, sect. 3.6 ). Ligger & Stubtletillities ( 2004, pp. 120 – 130 )</td></tr><tr><td>73. Adversarial search : Russell & Nonvig ( 2021, chpt. 5 )</td><td>74. Local or “ optimization ” search : Russell & Norvig ( 2021, chpt. 4 )</td></tr><tr><td>75. Singln Chauhan, Naqesh ( 128 December 2020 ). “ Optimization Algorithms in Neural Networks ” ( https :// www. ishsugaets. com / optimization = algorithms - en - neural - networks ). KDruggets. Retrieved 123 anuary 2024.</td><td>76. Evolutionary computation : Russell & Norvig ( 2021, sect. 4.1. 2 )</td></tr><tr><td>77. Merkle & Middendorf ( 2013 ).</td><td>78. Logic Russell & Norvig [ 2022, chpts. 6 – 9 ], Luger & Stubblefield [ 2004, pp. 35 – 77 ], Niloson ( 12909, chpt. 12 – 15 )</td></tr><tr><td>79 : Propositional logic : Rudsell & Norvig ( 2022, chpt. 6 ), Luger & Stubblefield ( 2004, pp. 45 – 50 ), NR5son ( 2399, chpt. 121 )</td><td>80. First - order logic and features such as equality : Ruzaell & Norvig [ 2021, chpt. T ], Poole, Mockworth & Geeteel [ 2993, pp. 298 – 275 ], Lugaer ± Stublefleld [ 2934, pp. 50 – 62 ], Nitson ( 1598, chpl. 25 )</td></tr><tr><td>81. Logical inference : Russell & Norvig ( 2021, chpt. 10 )</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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