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人工知能と機械学習 / AI and ML

Yukino Baba
September 26, 2019

人工知能と機械学習 / AI and ML

Yukino Baba

September 26, 2019
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  1. ஜ೾େֶ৘ใՊֶྨഅ৔ઇ೫
    CBCB!DTUTVLVCBBDKQ
    ஜ೾େֶڞ௨Պ໨ʢ৘ใʣ

    ʮσʔλαΠΤϯεʯ

    ਓ޻஌ೳͱػցֶश

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  2. /39
    ຊߨٛͰֶͿ͜ͱ
    w ਓ޻஌ೳ͕Ͳ͏͍͏৔໘Ͱ׆༻͞Ε͍ͯΔͷ͔
    w ͍·ͷਓ޻஌ೳʹ͸Կ͕Ͱ͖Δͷ͔
    w ػցֶशͰ͸ͲͷΑ͏ʹσʔλ͔ΒͷֶशΛߦ͏ͷ͔
    ਓ޻஌ೳʹԿ͕Ͱ͖Δͷ͔ɾػցֶश͸Ͳ͏͍͏͘͠Έ͔
    2

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  3. /39
    ਓ޻஌ೳͱ͸
    w ਓ޻஌ೳ͸

    ʮ஌తͳػցɺಛʹɺ஌తͳίϯϐϡʔλϓϩάϥϜΛ࡞ΔՊֶͱٕज़ʯ
    w ैདྷͷίϯϐϡʔλϓϩάϥϜ͸஌తͰ͸ͳ͔ͬͨ
    w ਓ͕ؒ஌ೳΛ࢖ͬͯͰ͖Δͷͱಉ͜͡ͱΛ

    ίϯϐϡʔλϓϩάϥϜͰ΋Ͱ͖ΔΑ͏ʹ͍ͨ͠
    ਓ޻஌ೳ͸ʮ஌తͳίϯϐϡʔλϓϩάϥϜʯ
    4

    IUUQTXXXBJHBLLBJPSKQXIBUTBJ"*GBRIUNM
    (*)

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  4. /39
    ਓ޻஌ೳͱ͸
    w ஌తͰ͸ͳ͍ίϯϐϡʔλϓϩάϥϜ͸

    ༩͑ΒΕͨϧʔϧͲ͓Γͷಈ࡞͔͠Ͱ͖ͳ͍
    ⿞ίϯϐϡʔλʹѻ͑ΔܗࣜͰਓ͕ؒϧʔϧΛ࡞Δඞཁ͕͋Δ
    ⿞ෳࡶͳ໰୊Ͱ͸͢΂ͯͷঢ়گΛ໢ཏͨ͠ϧʔϧΛ࡞Δ͜ͱ͸ࠔ೉
    w ஌తͳίϯϐϡʔλϓϩάϥϜʢਓ޻஌ೳʣ͸

    ঢ়گʹԠͯ͡ͲͷΑ͏ʹಈ࡞͢Ε͹ྑ͍͔ΛྟػԠมʹ൑அ͢Δ
    ⿞ܦݧ΍ڭࡐ͔ΒϧʔϧΛֶश͢Δ
    ⿞গ਺ͷϧʔϧ͔Βਪ࿦ͯ͠൑அ͢Δ
    ਓ޻஌ೳ͸ঢ়گʹԠͯ͡ྟػԠมʹಈ࡞͢Δ
    5

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  5. /39
    w ஌తͰ͸ͳ͍ίϯϐϡʔλϓϩάϥϜ͸ϧʔϧʹैͬͯೝࣝ͢Δ
    w ༷ʑͳච੻ͷखॻ͖จࣈΛ໢ཏͨ͠ϧʔϧΛ࡞Δͷ͸ࠔ೉


    ਓ޻஌ೳͱ͸
    ྫɿखॻ͖਺ࣈจࣈΛೝࣝ͢ΔίϯϐϡʔλϓϩάϥϜ
    6
    खॻ͖਺ࣈจࣈͷग़యɿIUUQZBOOMFDVODPNFYECNOJTU
    1
    1
    ʮࠨӈࡾ෼ͷҰͷྖҬ͕ന৭ͳΒʯ

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  6. /39
    ਓ޻஌ೳ͸ڭࡐʢը૾ͱ਺ࣈͷϖΞʣΛ࢖ͬͯೝࣝϧʔϧΛֶश͢Δ
    ਓ޻஌ೳͱ͸
    ྫɿखॻ͖਺ࣈจࣈΛೝࣝ͢ΔίϯϐϡʔλϓϩάϥϜ
    7
    1
    1
    9
    9
    7
    7
    ϧʔϧΛֶश
    9
    ϧʔϧΛར༻
    ڭࡐ
    ʜ
    खॻ͖਺ࣈจࣈͷग़యɿIUUQZBOOMFDVODPNFYECNOJTU

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  7. /39
    ೔ৗͷதͷਓ޻஌ೳ
    w εϚʔτεϐʔΧʔɿ

    ਓؒͷԻ੠ࢦࣔʹै͍༷ʑͳಈ࡞Λߦ͏
    w ϩϘοτ૟আػɿ

    ো֐෺Λආ͚ͳ͕Β෦԰શମΛ૟আ͢Δ
    w إೝূɿ

    Χϝϥલͷਓ෺͕ొ࿥͞ΕͨਓͱಉҰਓ෺͔Ͳ͏͔Λ൑ఆ
    w ίϯςϯπɾ঎඼ਪનɿ

    Ӿཡཤྺ΍ߪങཤྺʹ΋ͱ͖ͮϢʔβ͕޷ΉΞΠςϜΛఏࣔ
    զʑͷ೔ৗͷ͞·͟·ͳ৔໘Ͱਓ޻஌ೳ͕׆༻͞Ε͍ͯΔ
    8

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  8. /39
    ਓؒΛ௒͑Δਓ޻஌ೳ
    w *#.8BUTPOɿ

    ΞϝϦΧͷΫΠζ൪૊ʮ+FPQBSEZʯʹ௅ઓɺৗ࿈ग़ԋऀΛഁΓ༏উ
    w ౦ϩϘ͘Μɿ

    େֶೖࢼ໛ࢼͷ਺ֶɾੈք࢙Ͱภࠩ஋௒͑Λୡ੒
    w %FFQ#MVFɿ

    νΣεͷੈքνϟϯϐΦϯʹউར
    w "MQIB(Pɿ

    ғޟͷੈքτοϓع࢜ʹউར
    ΫΠζ΍ήʔϜͰਓؒΛ௒͑Δਓ޻஌ೳ͕ొ৔
    9

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    ݱࡏͷਓ޻஌ೳʹͰ͖Δ͜ͱɿಛԽܕWT൚༻ܕ
    w ਓ޻஌ೳ͸ʮಛԽܕʯͱʮ൚༻ܕʯͷೋछྨʹେผ͞ΕΔ
    w ಛԽܕਓ޻஌ೳɿ

    ͋Δಛఆͷঢ়گ΍໰୊ʹ͓͍ͯ஌తʹ;Δ·͏ਓ޻஌ೳ
    w ൚༻ܕਓ޻஌ೳɿ

    ਓؒͱಉ༷ʹ༷ʑͳঢ়گɾ໰୊ʹ͓͍ͯ஌తͳ;Δ·͍͕Ͱ͖Δਓ޻஌ೳ
    w ݱࡏͷਓ޻஌ೳ͸ಛԽܕਓ޻஌ೳͰ͋Γ

    ൚༻ܕਓ޻஌ೳ͸ະ࣮ͩݱ͞Ε͍ͯͳ͍

    ݱࡏͷਓ޻஌ೳ͸ಛఆͷ͜ͱ͚ͩͰ͖ΔಛԽܕਓ޻஌ೳ
    10

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  10. /39
    ݱࡏͷਓ޻஌ೳʹͰ͖Δ͜ͱɿը૾ॲཧ
    w ը૾ೝࣝɿ

    ࣸਅʹ͍ࣸͬͯΔ΋ͷΛೝࣝ͢Δ
    w ෺ମݕग़ɿ

    ࣸਅͷͲ͜ʹԿ͕͍ࣸͬͯΔͷ͔Λೝࣝ͢Δ
    w ը૾ੜ੒ɿ

    ຊ෺ͷࣸਅͷΑ͏ͳը૾Λਓ޻తʹ࡞Γग़͢
    ಛԽܕਓ޻஌ೳͰ΋༷ʑͳ͜ͱ͕࣮ݱͰ͖Δ
    11

    ग़యɿIUUQDTOTUBOGPSEFEVTMJEFTDTO@@MFDUVSFQEG


    ग़యɿIUUQTBSYJWPSHBCT
    ຊ෺ͷࣸਅ ਓ޻తͳࣸਅ
    (*)
    (*)

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  11. /39
    ݱࡏͷਓ޻஌ೳʹͰ͖Δ͜ͱɿݴޠॲཧ
    w จॻ෼ྨɿ

    จॻΛಛఆͷΧςΰϦʹ෼ྨ͢Δ
    w ػց຋༁ɿ

    ͋ΔݴޠͰॻ͔ΕͨจষΛผͷݴޠʹ຋༁͢Δ
    w ৘ใݕࡧɿ

    ಛఆͷ৘ใ͕ܝࡌ͞Ε͍ͯΔ΢ΣϒϖʔδΛݟ͚ͭΔ
    w ࣭໰Ԡ౴ɿ

    จষͰ༩͑ΒΕ࣭ͨ໰ʹର͢Δ౴͑Λฦ͢
    ಛԽܕਓ޻஌ೳͰ΋༷ʑͳ͜ͱ͕࣮ݱͰ͖Δ
    12

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  12. /39
    ݱࡏͷਓ޻஌ೳʹͰ͖Δ͜ͱɿԻ੠ॲཧ
    w Ի੠ೝࣝɿ

    ࿩͞Ε͍ͯΔ಺༰ΛจষͰॻ͖ى͜͢
    w ࿩ऀಉఆɿ

    ొ࿥͞Εͨਓ෺ͱಉ͡ਓ͕࿩͍ͯ͠Δ͔൑ఆ͢Δ
    w Ի੠߹੒ɿ

    ਓ͕ؒ࿩͍ͯ͠ΔΑ͏ͳ੠Λਓ޻తʹ࡞Γग़͢
    ಛԽܕਓ޻஌ೳͰ΋༷ʑͳ͜ͱ͕࣮ݱͰ͖Δ
    13

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  13. /39
    ݱࡏͷਓ޻஌ೳʹͰ͖Δ͜ͱɿϩϘοτ
    w ؀ڥೝࣝɿ

    ηϯα৘ใ౳͔ΒपғʹԿ͕͋Δ͔Λೝࣝ͢Δ
    w ܦ࿏ɾߦಈܭըɿ

    Ҡಈɾಈ࡞ࢦࣔʹै͏ͨΊʹͲͷΑ͏ͳܦ࿏Λ௨Δ͔ɺ

    Ͳͷ෦෼ΛͲͷΑ͏ʹಈ͔͔͢Λܭը͢Δ
    ಛԽܕਓ޻஌ೳͰ΋༷ʑͳ͜ͱ͕࣮ݱͰ͖Δ
    14

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    ਓ޻஌ೳͷ׆༻ྫ
    ྫᶃεϚʔτεϐʔΧʔ
    ಛԽܕਓ޻஌ೳͷ૊߹ͤͰ͞Βʹߴ౓ͳ͜ͱΛ࣮ݱͰ͖Δ
    15
    4UFQىಈΩʔϫʔυͷೝࣝ
    4UFQԻ੠ʹΑΔࢦࣔΛจষͱͯ͠ೝࣝ
    4UFQࢦࣔจ͔Β໨తΛೝࣝ
    4UFQࢦࣔΛ࣮ߦ

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    ਓ޻஌ೳͷ׆༻ྫ
    ྫᶄࣗಈӡస
    ಛԽܕਓ޻஌ೳͷ૊߹ͤͰ͞Βʹߴ౓ͳ͜ͱΛ࣮ݱͰ͖Δ
    16
    ը૾ͷग़యɿIUUQTTUPSBHFHPPHMFBQJTDPNTEDQSPEWTBGFUZSFQPSUXBZNPTBGFUZSFQPSUQEG
    4UFQଞͷं྆΍าߦऀ౳ͷݕ஌
    4UFQଞͷं྆΍าߦऀ౳ͷߦಈ༧ଌ
    4UFQ໨త஍·Ͱͷܦ࿏Λܭը
    4UFQܦ࿏ʹै͏ͨΊͷಈ࡞Λܭը

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  16. /39
    ਓ޻஌ೳͷ·ͱΊ
    w ਓ޻஌ೳ͸஌తͳίϯϐϡʔλϓϩάϥϜͰ

    ϧʔϧΛֶशɾਪ࿦ͯ͠ྟػԠมʹಈ࡞Ͱ͖Δ
    w ͍·ͷਓ޻஌ೳ͸ಛԽܕͰ͋Δಛఆͷ໰୊͔͠ղ͚ͳ͍
    w ༷ʑͳ෼໺ͰಛԽܕਓ޻஌ೳͷݚڀ͕ਐΊΒΕ

    ͦΕΒͷ૊߹ͤͰΑΓߴ౓ͳਓ޻஌ೳ͕࣮ݱ͞Εͭͭ͋Δ
    ͍·ͷਓ޻஌ೳ͸ಛԽܕ͕༷ͩʑͳ͜ͱ͕Ͱ͖Δ
    17

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  17. /39
    ػցֶशͱ͸
    w ػցֶश͸

    ίϯϐϡʔλϓϩάϥϜʹσʔλ͔ΒϧʔϧΛֶशͤ͞Δٕज़

    w େྔͷೖग़ྗྫͷσʔλΛڭࡐͱͯ͠༩͑

    ೖྗͱग़ྗΛରԠ͚ͮΔϧʔϧΛֶशͤ͞Δ
    ೖྗͱग़ྗΛରԠ͚ͮΔϧʔϧΛֶश͢Δ
    19

    ຊߨٛͰऔΓ্͛Δػցֶश͸ɺݫີʹ͸ʮڭࢣ෇͖ػցֶशʯͱݺ͹ΕΔ
    7
    ೖྗ ग़ྗ
    ը૾ ਺ࣈ

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    ػցֶशͱ͸
    ೖྗͱग़ྗΛରԠ͚ͮΔϧʔϧΛֶश͢Δ
    20
    The quick brown
    fox jumps over
    the lazy dog
    ૉૣ͍஡৭ͷޅ͸
    ͷΖ·ͳݘΛඈͼ
    ӽ͑Δ
    “Hello, world!”
    ೖྗ ग़ྗ
    ݈߁ϦεΫɿߴ

    ग़యɿIUUQDTOTUBOGPSEFEVTMJEFTDTO@@MFDUVSFQEG


    ग़యɿIUUQTNBHFOUBUFOTPSqPXPSHOTZOUIGBTUHFO
    จষ จষ
    ը૾ ෺ମͱҐஔ
    Ի੠ จষ
    ױऀ৘ใ ݈߁ϦεΫ
    (*)
    (**)

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  19. /39
    ػցֶशͱ͸
    w εϚʔτϑΥϯɾηϯαɾΠϯλʔωοτɾ΢ΣϒαʔϏε౳ͷීٴͰ

    σʔλ͕૿େͨ͠ͷ͕ػցֶशͷൃలͷ͖͔͚ͬ
    w ιʔγϟϧϝσΟΞ΁ͷ౤ߘ΍

    ΢ΣϒαʔϏε্Ͱͷਓʑͷߦಈϩά΋ػցֶशͷڭࡐͱͯ͠༻͍ΒΕΔ
    w ޿ࠂ഑৴ͳͲͰ΢ΣϒαʔϏεӡӦاۀ͸རӹΛ্͛ɺ

    ແྉͰ΢ΣϒαʔϏεΛఏڙ͠ͳ͕Βڭࡐ༻ͷσʔλΛऩू͍ͯ͠Δ
    ͋ΒΏΔ΋ͷ͕σʔλԽ͞ΕػցֶशͰ࢖ΘΕΔ
    21

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    ػցֶशͷ͘͠Έɿ܇࿅σʔλ
    w ྫ୊ɿʮजᙾ͕ྑੑ͔ѱੑ͔ʯ൑ఆ͢ΔͨΊͷϧʔϧΛֶशͤ͞Δ
    w ڭࡐͱͯ͠༻͍ΔσʔλΛ܇࿅σʔλͱݺͿ
    w ֶशͨ͠ϧʔϧΛ༻͍ͯ

    ܇࿅σʔλʹͳ͍जᙾ͕ྑੑ͔ѱੑ͔Λਖ਼͘͠൑ఆͰ͖ΔΑ͏ʹ͍ͨ͠

    ೖग़ྗྫΛ܇࿅σʔλͱͯ͠༻͍ͯϧʔϧΛֶश͢Δ
    22
    जᙾը૾ͷग़యɿW. N. Street et al. , Nuclear feature extraction for breast tumor diagnosis. Proc. of the International Symposium on Electronic
    Imaging, 1993.
    ྑੑ
    ѱੑ
    ྑੑ
    ѱੑ
    ܇࿅σʔλ ೖྗʢजᙾʣΛ

    ग़ྗʢྑੑPSѱੑʣʹ

    ରԠ͚ͮΔ

    ϧʔϧ
    ֶश

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  21. /39
    ػցֶशͷ͘͠Έɿ܇࿅σʔλ
    w ίϯϐϡʔλͰܭࢉͰ͖ΔΑ͏ʹ܇࿅σʔλ͸਺஋Ͱදݱ͞Ε͍ͯΔ
    w ྫɿʮେ͖͞ʯͱʮͰ͜΅͜౓ʯͰजᙾΛදݱ͢Δ

    ʢԿΒ͔ͷखஈͰ਺஋Խ͞Ε͍ͯΔͱ͢Δʣ
    ܇࿅σʔλ͸਺஋Ͱදݱ͞Εͳ͍ͱ͍͚ͳ͍
    23
    ܇࿅σʔλͷྫ
    ೖྗ
    ग़ྗ
    େ͖͞ Ͱ͜΅͜౓
    जᙾ 0.252 0.014 ྑੑ
    जᙾ 0.327 0.340 ྑੑ
    जᙾ 1.000 0.371 ѱੑ
    जᙾ 0.223 0.369 ѱੑ
    ʜ ʜ ʜ ʜ

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    ػցֶशͷ͘͠Έɿ܇࿅σʔλ
    ܇࿅σʔλ͸਺஋Ͱදݱ͞Εͳ͍ͱ͍͚ͳ͍
    24
    211 220 223 … 213
    217 217 220 … 210
    214 217 216 … 205
    … … … … …
    216 217 214 … 181
    The quick brown fox jumps
    over the lazy dog
    aardvark … brown … dog … zzzat
    0 … 1 … 1 … 0

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    ػցֶशͷ͘͠Έɿ܇࿅σʔλ
    ܇࿅σʔλ͸਺஋Ͱදݱ͞Εͳ͍ͱ͍͚ͳ͍
    25
    ܇࿅σʔλͷྫ
    ೖྗ
    ग़ྗ
    େ͖͞ Ͱ͜΅͜౓
    जᙾ 0.252 0.014 ྑੑ
    जᙾ 0.327 0.340 ྑੑ
    जᙾ 1.000 0.371 ѱੑ
    जᙾ 0.223 0.369 ѱੑ
    ʜ ʜ ʜ ʜ
    ਤࣔ
    େ͖͞
    Ͱ͜΅͜౓
    ྑੑͷजᙾ
    ѱੑͷजᙾ
    जᙾ
    जᙾ

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  24. /39
    ػցֶशͷ͘͠Έɿϧʔϧ
    w ίϯϐϡʔλ͕ܭࢉͰ͖ΔΑ͏ʹϧʔϧ΋਺ࣜͰදݱ͞ΕΔ
    w ୯७ͳϧʔϧͷܗࣜɿ
    ϧʔϧ΋਺ࣜͰදݱ͞Εͳ͍ͱ͍͚ͳ͍
    26
    େ͖͞ Ͱ͜΅͜౓ 

    ͳΒྑੑɺͦΕҎ֎ͳΒѱੑ
    z = w1
    × + w2
    × + b;
    z < 0

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    ػցֶशͷ͘͠Έɿϧʔϧ
    ϧʔϧ΋਺ࣜͰදݱ͞Εͳ͍ͱ͍͚ͳ͍
    27
    େ͖͞
    Ͱ͜΅͜౓
    ྑੑͷजᙾ
    ѱੑͷजᙾ
    [ʾͷྖҬ
    [ͷྖҬ
    ͷ৔߹ɿ

    େ͖͞ Ͱ͜΅͜౓
    w1
    = 1, w2
    = − 1, b = 0
    z = −
    େ͖͞ Ͱ͜΅͜౓
    z = w1
    × + w2
    × + b;

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    ػցֶशͷ͘͠Έɿϧʔϧ
    X X Cͷ஋͕มΘΔͱϧʔϧ΋มΘΔ
    28
    େ͖͞
    Ͱ͜΅͜౓
    ྑੑͷजᙾ
    ѱੑͷजᙾ
    [ʾͷྖҬ
    [ͷྖҬ
    ͷ৔߹ɿ

    େ͖͞ Ͱ͜΅͜౓
    w1
    = 1, w2
    = 1, b = − 1
    z = + −1
    େ͖͞ Ͱ͜΅͜౓
    z = w1
    × + w2
    × + b;

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    ػցֶशͷ͘͠Έɿϧʔϧ
    ܇࿅σʔλʹ౰ͯ͸·Δྑ͍ϧʔϧΛֶश͍ͨ͠
    29
    େ͖͞
    Ͱ͜΅͜౓
    େ͖͞
    Ͱ͜΅͜౓

    w1
    = 1, w2
    = − 1, b = 0
    w1
    = 1, w2
    = 1, b = − 1
    ϧʔϧ͕౰ͯ͸·Βͳ͍जᙾ
    ӈͷϧʔϧͷํ͕ɺ܇࿅σʔλʹ౰ͯ͸·Δ

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    ػցֶशͷ͘͠Έɿଛࣦ
    ϧʔϧͷྑ͠ѱ͠Λ఺਺Ͱද͢
    30
    ଛࣦɿେ ଛࣦɿখ
    େ͖͞
    Ͱ͜΅͜౓
    େ͖͞
    Ͱ͜΅͜౓
    w ଛࣦɿϧʔϧͷѱ͞Λ఺਺Ͱදͨ͠΋ͷ
    w ܇࿅σʔλʹ౰ͯ͸·Βͳ͍ϧʔϧ͸ଛࣦ͕େ͖͘ͳΔ

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    ػցֶशͷ͘͠Έɿଛࣦ
    w ʮޡ൑ఆͷ݅਺ʯΛଛࣦͱͯ͠࢖͏ͱϧʔϧͷࡉ͔͍ҧ͍͕Θ͔Βͳ͍
    w ϧʔϧʹʮѱੑͷ֬཰ʯΛग़ྗͤ͞ɺͦΕʹ΋ͱ͖ͮଛࣦΛࢉग़
    ϧʔϧͷྑ͠ѱ͠Λ఺਺Ͱද͢
    31
    ૯࿨Λϧʔϧͷଛࣦͱ͢Δ
    ଛࣦɿ
    ܇࿅σʔλ
    [
    ѱੑͷ֬཰

    ଛࣦ
    ೖྗ
    ग़ྗ
    େ͖͞ Ͱ͜΅͜౓
    जᙾ 0.252 0.014 ྑੑ -0.542 0.336 0.410
    जᙾ 0.327 0.340 ྑੑ -0.172 0.457 0.611
    जᙾ 1.000 0.371 ѱੑ 1.208 0.770 0.261
    जᙾ 0.223 0.369 ѱੑ -0.348 0.414 0.882
    ʜ ʜ ʜ ʜ
    a = σ(z)

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  30. /39
    ػցֶशͷ͘͠Έɿޯ഑๏
    w ଛࣦ͕࠷খͷϧʔϧɺ

    ͭ·Γ܇࿅σʔλʹ࠷΋౰ͯ͸·ΔϧʔϧΛݟ͚ͭΔͷֶ͕श
    w ޮ཰తʹͦͷΑ͏ͳϧʔϧΛݟ͚ͭΔͨΊʹޯ഑๏͕༻͍ΒΕΔ
    w ޯ഑๏Ͱ͸ɺଛࣦ͕࠷΋খ͘͞ͳΔํ޲ʹগ͚ͩ͠ਐΉ

    ʢX X Cͷ஋Λগ͚ͩ͠มԽͤ͞Δʣ͜ͱΛ܁Γฦ͢
    ଛࣦ͕࠷খͱͳΔϧʔϧʢX X Cͷ஋ʣΛݟ͚ͭΔ
    32

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    ػցֶशͷ͘͠Έɿ൚Խೳྗ
    w ֶशͷͦ΋ͦ΋ͷ໨త͸

    ʮ܇࿅σʔλʹͳ͍ະ஌ͷೖྗʹରͯ͠ਖ਼͍͠ग़ྗΛฦ͢ʯϧʔϧͷֶश
    w ܇࿅σʔλ΁ͷ౰ͯ͸·Γ͚ͩΛߟྀ͢Δͱ

    ϧʔϧ͕܇࿅σʔλʹ౰ͯ͸·Γ͗ͯ͢ະ஌ͷೖྗͰ͸ؒҧ͑ΔڪΕ
    w X Xͷ஋͕ۃ୺ʹେ͖͍ϧʔϧ͸ա৒ద߹͍ͯ͠ΔڪΕ͕͋ΔͷͰ

    ͦͷΑ͏ͳϧʔϧʹϖφϧςΟΛ༩͑ΔΑ͏ʹଛࣦΛઃܭ͢Δ
    w ʮϧʔϧ͕ະ஌ͷೖྗʹର͠ਖ਼͍͠ग़ྗΛฦ͢ೳྗʯΛ

    ൚Խೳྗͱ͍͏
    ܇࿅σʔλʹ౰ͯ͸·Γ͗͢Δϧʔϧ͸൚Խೳྗ͕௿͍
    33

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    w ௚ઢͰදݱ͞ΕΔϧʔϧ͸ೖྗͱग़ྗͷରԠ͚͕ͮෳࡶͳ৔߹ʹෆे෼
    w ਂ૚ֶशΛ༻͍ΔͱෳࡶͳϧʔϧΛֶशͰ͖Δ
    େ͖͞
    Ͱ͜΅͜౓
    େ͖͞
    Ͱ͜΅͜౓
    ୯७ͳϧʔϧ
    ػցֶशͷ͘͠Έɿਂ૚ֶश
    ෳࡶͳϧʔϧͷֶशʹ༻͍ΒΕΔͷ͕ਂ૚ֶश
    34
    େ͖͞
    Ͱ͜΅͜౓
    ਂ૚ֶशʹΑΔϧʔϧ

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    w ୯७ͳϧʔϧͷ໛ࣜਤɿ
    ػցֶशͷ͘͠Έɿਂ૚ֶश
    ਂ૚ֶशͰ͸ෳ਺ͷඇઢܗؔ਺Λଟ૚ʹॏͶΔ
    35
    େ͖͞
    Ͱ͜΅͜౓
    ྑੑ·ͨ͸ѱੑ
    w1
    w2
    a=σ(z)
    େ͖͞
    Ͱ͜΅͜౓
    ྑੑ·ͨ͸ѱੑ
    w ਂ૚ֶशʹΑΔϧʔϧͷ໛ࣜਤɿ

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  34. /39
    ػցֶशͷ࣮૷
    w ػցֶशͷ࣮૷ʹ͸ɺϓϩάϥϛϯάݴޠ1ZUIPO͕޿͘࢖ΘΕ͍ͯΔ
    w ྫ͑͹TDJLJUMFBSOͱ͍͏1ZUIPOϥΠϒϥϦΛ࢖͏ͱɺ

    ਺ߦͷίʔυͰػցֶशΛ࣮૷͢Δ͜ͱ͕Ͱ͖Δ
    w ͍͔ͭ͘ͷαϯϓϧσʔλ΋TDJLJUMFBSOͰఏڙ͞Ε͍ͯΔ
    ༷ʑͳϥΠϒϥϦ͕ެ։͞Ε͓ͯΓ؆୯ʹ࣮૷͕Ͱ͖Δ
    36
    https://scikit-learn.org/
    ϧʔϧͷֶश
    ϧʔϧͷద༻

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  35. /39
    ػցֶशͷ࣮૷
    w ਂ૚ֶशͷ1ZUIPOϥΠϒϥϦ΋༷ʑͳ΋ͷ͕ఏڙ͞Ε͍ͯΔɿ

    5FOTPS'MPX ,FSBT 1Z5PSDI $IBJOFS
    w (PPHMF$PMBCPSBUPSZΛ࢖͏ͱԾ૝ϚγϯΛ࢖ͬͯ

    ਂ૚ֶशϥΠϒϥϦΛ΢Σϒϒϥ΢β͔Β؆୯ʹ࢖͏͜ͱ͕Ͱ͖Δ
    ༷ʑͳϥΠϒϥϦ͕ެ։͞Ε͓ͯΓ؆୯ʹ࣮૷͕Ͱ͖Δ
    37
    https://colab.research.google.com

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  36. /39
    ػցֶशͷ࣮૷
    w ,BHHMFͳͲͷػցֶशίϯϖςΟγϣϯͰ͸

    ࣮ફతͳػցֶशͷ՝୊ɾσʔλ͕ఏڙ͞Εଞऀͱڝ͏͜ͱ͕Ͱ͖Δ
    ػցֶशίϯϖςΟγϣϯͰྗࢼ͠Λ͢Δ͜ͱ͕Ͱ͖Δ
    38
    https://www.kaggle.com/c/titanic/leaderboard

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  37. /39
    ػցֶशͷ·ͱΊ
    w େྔͷೖग़ྗྫͷσʔλΛ܇࿅σʔλͱͯ͠࢖͍

    ೖྗͱग़ྗΛରԠ͚ͮΔϧʔϧΛֶश
    w ܇࿅σʔλʹͰ͖Δ͚ͩ౰ͯ͸·ΔΑ͏ͳϧʔϧ

    ʢଛࣦͷখ͍͞ϧʔϧʣΛޯ഑๏Λ༻͍ͯݟ͚ͭΔ
    ܇࿅σʔλΛ༻͍ͯೖྗͱग़ྗΛରԠ͚ͮΔϧʔϧΛֶश
    39

    View full-size slide