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The Mathematics of Neural Networks

The Mathematics of Neural Networks

Tutorial talk at the conference F2S "Science et Progrès" 2023

Gabriel Peyré

May 05, 2023
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  1. The Mathematics of


    Neural Networks
    Gabriel Peyré
    É C O L E N O R M A L E
    S U P É R I E U R E
    www.numerical-tours.com

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  2. Overview
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    Convolutional 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
    ResNet 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
    Transformers
    Networks Architectures
    Optimization
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    non-linear
    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
    structured
    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
    very deep
    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
    very non-linear
    Machine Learning
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    f✓
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    x
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    y
    yi = +1
    yi = 1

    View full-size slide

  3. Supervised vs Unsupervised Learning
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    Un-supervised learning:
    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
    Clustering AABE9XictVzdchPJFW42fxvyx2YvczMbLyk2RYhxyE/VVqoWLDBevGCQbNjFQGmksRCMNEIjyYDWj5LKTSqVXOUR8hx5gFQlV3mFnJ/u6R6pZ06PQ5iy3dPT3zmnz3SfPud0D/EkHeazzc1/nHvvG9/81re/8/53z3/v+z/44Y8ufPDjwzybT3vJQS9Ls+mjuJsn6XCcHMyGszR5NJkm3VGcJg/jl9v4/OEimebDbNyZvZkkT0bdwXh4POx1Z1D17MKHO8k4mcLNIolGWR/pDJ5d2Ni8skn/ovXCVV3YUPrffvbBR/9UR6qvMtVTczVSiRqrGZRT1VU5XI/VVbWpJlD3RC2hbgqlIT1P1Kk6D9g5tEqgRRdqX8LvAdw91rVjuEeaOaF7wCWFnykgI3URMBm0m0IZuUX0fE6UsbaK9pJoomxv4G+saY2gdqaeQ62EMy1DcdiXmTpWv6M+DKFPE6rB3vU0lTlpBSWPnF7NgMIE6rDch+dTKPcIafQcESanvqNuu/T8X9QSa/G+p9vO1b9JyotwRaqte58VFLpqQfQjeptzeMbypMB5ABQS3UcsnZCuR9T7MbRfQv1duE6pZHQSw7Wk2tNa5DZcPuS2iNyBy4fcEZF7cPmQeyJyHy4fcl8jETslnfvxbbh8+LbI+T5cPuR9EfkALh/ygYg8hMuHPBSRX8HlQ34lIm/B5UPeEpF34PIh74jIDlw+ZEdEHsDlQx6IyJtw+ZA3NbJ6pk7hyojOUJiV16Fc5oGWIoWa66J8N8g6+rA3AuZ0rwIrz+oW/PVjWwE6TSqwNwPG3XEFVh55O2Aj/VjZFt2m1cSHvS1id2EE+LG7IvZz9aIC+3nATHtZgZXn2h6082Nl6/sF3PmxX4jYu1DyY+U16h7U+LH3AlaMSQV2X8TeV68qsCFWf1qBle1+G+yKHyuvUx1o78eGWNN5BVa2p4fgwfix8mr1EGr92Ici9pF6XYF9JGK/BOvux34ZsMK+rcCaNfY8rSAD8kcSmLF11LrFrMTSBKh1Bf5psbak5BvHUC9hBgVmQJiRiNgpEDuBiL0CsRcsV17Y0Zz8XZlLu0C0AxFxsTZhaSa27xftsZQGIFoForWCqPNI8V2bvizIuzA1EnJWrFxYCulTVthvLCV6PNRbXoO4V0Lw2H5OI/8yRUsYQaGm6qg9L9Z4RkZ0X4c4oejN9NLwkHGzwiq4qNciKvagYhH1xoN6I6LmHtRcRC08qIWIsjPfxR0FjACrf3wXS7rjEcA+cvUVgVdwHVad2zBHIxg/++AFPqCae/C3TbG3dNVJhtE8rpOY5XhSssRTKC3VBtTbqLBF8XVKMywBybjlPR3j4x3mNpZ6zrEVPi1W8qjImITTGZI8g4IOeosRzadmdO5QzSl5d1xqhr9dzHtTaoa/SRo/JS+eS83wMy397AyydzS2cwZsG2bTRGvflpvS4PwL0zDl87TqosXFtzrSYwbpvW5If1e/md0zvJdtKrF+bLkZjdzpX17qXxMaVs+5o+dmVNB7Yq/XlKLGPRnruNeWm8qQ0So61nLYu6ZvBtv09Zsx5WY09sHj2qaYe+mUm47eSdEbW25G41Bx3vOUPHlTbkZjQPesD1tuRgOzLV0d59tyU8uOGuDY2ZabWvUxZYExB8RjnmusVzQlP2muqQ3JP6jP1rg+//o6hjmbp0WMUE/J+rbVdOJiLauXyPgLCVi1WUM50L+YOz5YmcZSbYnxFcswK63v63TsGo+a3wMtRjD7eQ9AypmnIKHJSaD1ToHiVTHqKvfM4LZEHI6S4xXUka6did6i5ctZo3LdM6qV4jLbW6vHI7LXOY29CfmEe6RZSQ97lW+4iqKkob2ShmR6TXT3Vs/XsvY3RdxkBTEpRlqPdoR4J60+TvVpve3o+KLe5ZnBxXs+dvxitvlYWxuMeTKyRShLHU+3nckjuXW4rl5WNsfNzyJ6o2ivFmQ1hrQjlYtRqMkWsze+pHtL+4D25JAH0+jBe4w0lYniXTPMomM+PSKL6tpbiTfqy2TouJyT1TX2uB49cNADD7p5jLMNK8ZdKHUgZjiAu05AlHO+0FVGGp+qXxS7oxm9wfqIPi1ZSEOD7U1SspB1UfbzEpUTQONo4Cg9nMYqHYM/WqMkR/0+eWzsWrb8F2nn1uxvd2mMV4/m6kxMn7huEdeIZg3v6vLdKgeWYOl9skX+a30vkV8TjmhDJa5PHc6slzHt+CcUwU7IM05ptkmzo9zazU+tPjGc9pXZO8fd7IwsZET2L4L1KaMxGdGPe3bA7KCzRUjJRobYnWHh3fh8naE4xqwfN1R8qsGOt4Rs2Zz4G7ru7MppLHLEwOvA6crYNjrZI18wIa5Tbd3t3K5ffRBpz0m4o4Qp2rFyifh/Qr/NjxknG2sjAjWMbyDXts73PjKKWVBHXVrl622QaetK+XEhw1MttV3/rEwflyRrUcSF8uBq3QfOPbpnXjhKpiR3vtaG19G6bC5SnqzoEXt7TFE82/2BXoFR7su0Sm7QnDuiUTKAUTArogjTVsoir/Kt51WmHkY7/79Qt7ouaw0pRspmcFlDUn4/oWjNlTKFUc3j9yXNJr/Wpyut6vmMaSyOnLn8NdR+BL+N3OY+jE5csgo3aAwwBXtnNcI10VqLMF43SrzMyDS07L3lZ8ekaeXWnCW+ZutmY+xFYyr7NGpe66yFKZ+FxguHxotAHXZor9Fq0dQbS/RMjC06ercylF8Tbp0GlOciZdkjM6hhgJRuLBVGtS9SlWN8g3or0toUaXVhtrq7Ae6cD0H65/rq7P66WN0jdYt8mx55YBy/9GmWDsnnMrX1kRpTQM7XtH11Z/8R1SD3mCwoUuZznDhjeNepR9dpIenP9MqWkZ23FsGcWzrRbYyNPaLyr9aQI5oTOc1Lg7hGLRItvytHtGKRrjg+R0SZ/y75VOx31MfMbmv7TqKSP2HjTZ5VlhdHCmPSv5R5212LXned+DWimHCuvesYaDV/w0iBMSaT4Pcsc3pDuMrxTgJ7tDHZz3U7xbt4Y0eiKyT1Uv0+wMZw1GvHuju2TI9N334OLVHr9q37Wsj80mCOEr+z7Oh1aVUbaR91uXJ/NlpdvcqV7+v0MF/ha/UxpzZuZGGjvDLmSH0azIUlasaFMSFcmvWiifzNJG8iM+9OhVI2rQ3lcqaBbcxzipekc6CI8Hl3l7ze3CdCP+I1ejFhXWpcI1HCbFym8wOupcWsVLQSIbn10pqUOutR1XphebirhrXjbCkTsoKpknI33Nrtw1EpWpGzMUyhp/hkb1Wc6NL8FC78HSlflGg4huQQ2+DnXlfb6uY7OBXxSpc5sxlRDdqE/koM3tX9LLeo19Erh7pLP4RDOI8h6FqSfkgralPZmbIsuUs9nP4JWYOpSkTpbcvmfXC5yD1Z59SkP0OycHJvhsp8k9O0L4ZDSE/KXML58P6G1ItjZb5tatYHQ13uQZlDEx7mPEPYO7etm/NyOdXra51LKA9eB8zOi8HhDmB1zGLbhVioqfNG3j0HtA7HNdTNavG/9sPwsZya8wrlltM3Zy8C3jq3S3RmFv3i5nPGcgsZzdUcw3lmRe+s1+Tnx/5f1OhNZU5v3j199EvtGDC8lorzobJ0jHdHkZU3lAruD/hkyNR/1N/PyV8lvCpoVMnRhJLZr6imZlrI1MyXl77emWchMlk6VTKVqdl4ok0nY7fVrroFP9uFB9j0lCh/U8l/Eev/jrYPtcdkPUw2nTMIR1SXUBbE7qb16d6eo62SGM/08hnfDtTgnvge1eJ537vUHs/8dkp9q/6ShOf6FypT/VJksrrLZ+dVDD0o78BxLsh87xvRmXrOZvEJtFHAHiOfo+JIyXz9vCREn+LCVUmXhDCjpY5y7KUc05mkpIJ2XOpbj0b4RO/0474Dns/vFtmlSP2S6rp6dcCVWpJq3yPVY8oMxKT/TYjQfq0uw9/LuuyXdH9N0pzeQVmi186z+pNgp95xYb9mvEh5MJOpW+h2GUX1dvewPhPbquTCJ97r8YMa/MCRsk1v6yXF3VNVnzuc19Cca5nc/dyxMnlP1gNGs91ifNTHz4saXouA/t+pRN9xJN0BWWLKtke0nzcleqnWzU2Sns9V1udtb9dIa77aZJr2ZKUdB+aMZP2eQKrHXfXs53OQUq4mqaDjznU+kSmdFhl6KcnzcxJwGqIb0Fu5ryE9lajMRUnmAV8iLwJkWQTQORakORYpDERJtH14dmHj6ur/9bFeONy6cvU3V67d39r47Ib+f0DeVz9RP1WXYO37rfoMxv++OgBOb9Qf1V/UX1snrT+0/tT6Mzd975zGfKhK/1p/+y9pN0fB
    Generative modeling
    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
    Dimensionality reduction
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    =)
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    =)
    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
    VAE, GAN, di↵usion
    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
    k-means, DBScan
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    PCA, t-SNE, UMAP

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  4. Supervised vs Unsupervised Learning
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    Un-supervised learning:
    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
    Clustering 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
    Generative modeling
    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
    Dimensionality reduction
    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
    Supervised learning:
    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
    Regression
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    ?
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    =)
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    Classification
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    ?
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    =)
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    y = +1
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    y = 1
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    =)
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    =)
    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
    VAE, GAN, di↵usion
    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
    k-means, DBScan
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    PCA, t-SNE, UMAP

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  5. Supervised vs Unsupervised Learning
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    Un-supervised learning:
    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
    Clustering 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
    Generative modeling
    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
    Dimensionality reduction
    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
    Supervised learning:
    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
    Regression
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    ?
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    =)
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    Classification
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    ?
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    =)
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    y = +1
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    y = 1
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    =)
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    =)
    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
    VAE, GAN, di↵usion
    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
    k-means, DBScan
    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
    PCA, t-SNE, UMAP
    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
    Self-supervised learning:
    Demain, dès l'aube, à
    l'heure où blanchit la
    campagne, Je partirai. Vois-
    tu, je sais que tu
    m’attends. J'irai par la
    forêt, j'irai par la
    montagne. Je ne puis
    demeurer loin de toi plus
    longtemps. Je marcherai les
    yeux fixés sur mes pensées,…
    Demain, dès l'aube, à
    l'heure où blanchit la
    campagne, Je partirai. Vois-
    tu, je sais que tu
    m’attends. J'irai par la
    forêt, j'irai par la
    montagne. Je ne puis
    demeurer loin de toi plus
    longtemps. Je marcherai les
    yeux fixés sur mes pensées,…
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    ? ? ?
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    =)

    View full-size slide

  6. Empirical Risk Minimization
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    Dataset: (xi, yi)n
    i=1
    .
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    yi
    2 R
    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
    y ⇡ f✓(x)
    AABE/nictVxZcxxJES4v12IuLzzCQy9aE94NY2RhjmCDiLU1sqy11pY9I9m7lu2YozVuq2d6PD0jH7OKIPgxBC8EAU+88Tv4AUTAE3+BPKq6qmeqO6uFcYek6ur6MrOyq7Iys6rdm6RJPltf/8e5d77y1a99/RvvfvP8t779ne9+78J73z/Is/m0H+/3szSbPux18zhNxvH+LJml8cPJNO6Oemn8oHe8ic8fnMTTPMnGndnrSfx41B2Ok6Ok351B1dMLP9oaTZIp3KbRNMmPo1EyTkbJG3r6m6cX1tavrNO/aLVwVRfWlP63l733/j/VoRqoTPXVXI1UrMZqBuVUdVUO1yN1Va2rCdQ9Vguom0IpoeexOlXnATuHVjG06ELtMfwewt0jXTuGe6SZE7oPXFL4mQIyUhcBk0G7KZSRW0TP50QZa6toL4gmyvYa/vY0rRHUztQzqJVwpmUoDvsyU0fq19SHBPo0oRrsXV9TmZNWUPLI6dUMKEygDssDeD6Fcp+QRs8RYXLqO+q2S8//RS2xFu/7uu1c/ZukvAhXpNq691lBoatOiH5Eb3MOz1ieFDgPgUKs+4ill6TrEfV+DO0XUH8HrlMqGZ304FpQ7WktchMuH3JTRG7D5UNui8hduHzIXRG5B5cPuaeRiJ2Szv34Nlw+fFvkfA8uH/KeiLwPlw95X0QewOVDHojIL+DyIb8QkTfh8iFvisjbcPmQt0VkBy4fsiMi9+HyIfdF5BZcPuSWRlbP1ClcGdFJhFl5HcplHmgpUqi5Lsp3g6yjD3sjYE73K7DyrG7BXz+2FaDTuAK7FTDujiqw8sjbBhvpx8q26BatJj7sLRG7AyPAj90RsZ+q5xXYTwNm2nEFVp5ru9DOj5Wt72dw58d+JmLvQMmPldeou1Djx94NWDEmFdg9EXtPvajAhlj9aQVWtvttsCt+rLxOdaC9HxtiTecVWNmeHoAH48fKq9UDqPVjH4jYh+pVBfahiP0crLsf+3nACvumAmvW2PO0ggzJH4lhxtZR6xazEksToNYV+KfF2pKSb9yDegkzLDBDwoxExHaB2A5E7BaI3WC58sKO5uTvylzaBaIdiOgVaxOWZmL7QdEeS2kAolUgWkuIOo8U37Xpywl5F6ZGQs6KlQtLIX3KCvuNpViPh3rLaxB3Swge289o5F+maAkjKNRUHbVnxRrPyIju6xAvKXozvTQ8ZNyssAou6pWI6nlQPRH12oN6LaLmHtRcRJ14UCciys58F3cYMAKs/vFdLOiORwD7yNVXBF7BdVh1bsEcjWD87IEXeJ9q7sLfNsXe0lUnGUbzuE5iluNxyRJPobRQa1Bvo8IWxdcpzbAYJOOWd3WMj3eY21joOcdW+LRYyaMiYxJOJyF5hgUd9BYjmk/N6NymmlPy7rjUDH+rmPem1Ay/RRo/JS+eS83wMy397AyydzS2cwZsG2bTRGvflpvS4PwL0zDl87TqosXFtzrSYwbpvWpIf0e/mZ0zvJdNKrF+bLkZjdzpX17qXxMaVs+5o+dmVNB7Yq/XlKLGPRnruNeWm8qQ0So61nLYu6ZvBtsM9Jsx5WY09sDj2qSYe+GUm47eSdEbW25G40Bx3vOUPHlTbkZjSPesD1tuRgOzLV0d59tyU8uOGuDY2ZabWvUxZYExB8RjnmusVzQlP2muqSXkH9Rna1yff3Udw5zNkyJGqKdkfdtqOr1iLauXyPgLMVi1WUM50L+YOz5YmcZCbYjxFcswK63vq3TsGo+a3wUtRjD7eQ9AypmnIKHJSaD1ToHiVTHqKvfM4DZEHI6SoyXUoa6did6i5ctZo3LdU6qV4jLbW6vHQ7LXOY29CfmEu6RZSQ+7lW+4iqKkod2ShmR6TXT3Rs/XsvbXRdxkCTEpRlqfdoR4J60+TvVpve3o+KLe5ZnBxXs+dvxitvlIWxuMeTKyRShLHU+3nckjuXW4rl5WNsfNzyJ6o2ivTshqJLQjlYtRqMkWsze+oHtLe5/25JAH0+jDe4w0lYniXTPMomM+PSKL6tpbiTfqy2TouJyT1TX2uB49dNBDD7p5jLMJK8YdKHUgZtiHu05AlHO+0FVGGp+qnxa7oxm9wfqIPi1ZSEOD7U1cspB1UfazEpWXgMbRwFF6OI1lOgZ/uEJJjvp98tjYtWz5L9LOrdnf7tIYrx7N1ZmYAXHdIK4RzRre1eW7ZQ4swcL7ZIP81/peIr8mHNGGSlyfOJxZL2Pa8Y8pgp2QZ5zSbJNmR7m1m59afmI47Smzd4672RlZyIjsXwTrU0ZjMqIf9+yA2UFni5CSjQyxO0nh3fh8nUQcY9aPSxSfarDjLSZbNif+hq47u3Iaixwx8DpwujS2jU52yReMietUW3c7t+tXH0TacxLuKGGKdqxcIv4f0m/zY8bJ2sqIQA3jG8i1rfO9j4xiFtRRl1b5ehtk2rpSflDI8ERLbdc/K9MHJclaFHGhPLhaD4Bzn+6ZF46SKcmdr7ThdbQum4uUJ0t6xN4eURTPdn+oV2CU+zKtkms05w5plAxhFMyKKMK0lbLIy3zreZWph9HO/y/Ura7LWkOKkbIZXNaQlN+PKVpzpUxhVPP4PabZ5Nf6dKlVPZ8xjcWRM5e/hNr34beR29yH0emVrMINGgNMwd5ZjXBNtNIijNeNEi8zMg0te2/52TFpWrk1Z4mv2brZGPukMZU9GjWvdNbClM9C47lD43mgDju012i1aOqNJXoqxhYdvVsZyq8Jt04DynORsuyRGVQSIKUbS4VRHYhU5RjfoN6ItNZFWl2Yre5ugDvnQ5D+ub48u78sVvdI3STfpk8eGMcvA5qlCflcprY+UmMKyPmatq/u7D+kGuTeIwuKlPkcJ84Y3nXq03VaSPoTvbJlZOetRTDnll7qNsbGHlL55yvIEc2JnOalQVyjFrGW35UjWrJIVxyfI6LMf5d8KvY76mNmt7V9J1HJn7DxJs8qy4sjhTHpX8q87axErztO/BpRTDjX3nUPaDV/w0iBMSaT4Pcsc3pDuMrxTgJ7tD2yn6t2infxxo5EV0jqhfptgI3hqNeOdXdsmR6bvn0ELVHr9q37Wsj80mCOEr+z7Oh1aVUbaR91sXR/NlpdvcqV7+v0MF/ia/UxpzZuZGGjvDLmUH0czIUlasaFMSFcmvWiifzNJG8iM+9OhVI2rQ3lcqaBbcwzipekc6CI8Hl3l7ze3IdCP3or9HqEdalxjUQJs3GZzg+4lhazUtFShOTWS2tS6qxHVeuF5eGuGtaOs6WMyQqmSsrdcGu3D4elaEXOxjCFvuKTvVVxokvzY7jwd6R8UaLhGJJDbIOfe11tqq23cCrihS5zZjOiGrQJg6UYvKv7WW5Rr6MXDnWXfgiHcB4J6FqSPqEVtansTFmW3KUeTv8lWYOpikXpbcvmfXC5yD1Z5dSkPwlZOLk3iTLf5DTti+EQ0pMyl3A+vL8h9eJImW+bmvXBUJd7UObQhIc5zxD2zm3r5rxcTvX6WuUSyoPXAbPzYnC4A1gds9h2IRZq6ryRt88BrcNRDXWzWvyv/TB8LKfmvEK55fTN2fOAt87tYp2ZRb+4+Zyx3EJGczXHcJ5Z0TvrNfn5sf8XNXpTmdObt08f/VI7BgyvheJ8qCwd491RZOUNpYL7Az4ZMvUf9fdz8lcJLwoaVXI0oWT2K6qpmRYyNfPlpa935lmITJZOlUxlajaeaNPJ2E21o27Cz2bhATY9JcrfVPJfxPq/ox1A7RFZD5NN5wzCIdXFlAWxu2kDurfnaKskxjO9fMa3AzW4J75LtXje9w61xzO/nVLfqr8k4bn+mcrUoBSZLO/y2XnVgx6Ud+A4F2S+943oTD1ns/gE2ihgj5HPUXGkZL5+XhBiQHHhsqQLQpjRUke556XcozNJcQXtXqlvfRrhE73Tj/sOeD6/W2SXIvUzquvq1QFXakmqPY9Ujygz0CP9r0OE9gt1Gf5e1mW/pHsrkub0DsoSvXKe1Z8EO/WOC/s140XKg5lM3Ylul1FUb3cP6zOxrUoufOK9Hj+swQ8dKdv0to4p7p6q+tzhvIbmXMvk7ueOlcl7sh4wmu0W46M+fj6p4XUS0P/blejbjqTbIEuPsu0R7edNiV6qdbNF0vO5yvq87a0aac1Xm0zTnqy048CckazfE0j1uKue/XwOUsrVxBV03LnOJzKl0yKJl5I8PycBpyG6Ab2V+xrSU4nKXJRkHvAl8kmALCcBdI4EaY5ECkNREm0fnl5Yu7r8f32sFg42rlz95ZVr9zbWPrmh/x+Qd9UP1Y/VJVj7fqU+gfG/p/aB0+/VH9Vf1F9bv2v9ofWn1p+56TvnNOYHqvSv9bf/ArmWS4M=
    Empirical risk minimization:
    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
    min

    1
    n
    n
    X
    i=1
    `(f✓(xi), yi)
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    f✓
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    y
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    Regression: yi
    2 R
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    Least square:
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    `(y, y0) = (y y0)2

    View full-size slide

  7. Empirical Risk Minimization
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    Dataset: (xi, yi)n
    i=1
    .
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    yi
    2 R
    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
    y ⇡ f✓(x)
    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
    Empirical risk minimization:
    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
    min

    1
    n
    n
    X
    i=1
    `(f✓(xi), yi)
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    f✓
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    y
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    Regression: yi
    2 R
    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
    Least square:
    AABE+nictVxLcxTJES7WrzV+sfbFEb70WmCDQ4uFjB8RGxuxoBGgRYBgRoJdBoh5tIaB1vQwPTMgBvnHOHxxOOyT7/4d/gGOsE/+C85HVVf1THVntYzpkFRdXV9mVnZVVmZWNd1xMsymGxv/OPPB177+jW9+68Nvn/3Od7/3/R+c++iHB1k6m/Ti/V6apJNH3U4WJ8NRvD8dTpP40XgSd466Sfyw+3ILnz+cx5NsmI5a0+Nx/OSoMxgND4e9zhSqnp37cXS+HSfJxeP1459fij6LLh5/AoWnm+efnVvbuLxB/6LVwhVdWFP631760cf/VG3VV6nqqZk6UrEaqSmUE9VRGVyP1RW1ocZQ90QtoG4CpSE9j9WJOgvYGbSKoUUHal/C7wHcPda1I7hHmhmhe8AlgZ8JICN1ATAptJtAGblF9HxGlLG2jPaCaKJsx/C3q2kdQe1UPYdaCWdahuKwL1N1qH5HfRhCn8ZUg73raSoz0gpKHjm9mgKFMdRhuQ/PJ1DuEdLoOSJMRn1H3Xbo+b+oJdbifU+3nal/k5QX4IpUU/c+zSl01JzoR/Q2Z/CM5UmA8wAoxLqPWHpNuj6i3o+g/QLq78J1QiWjky5cC6o9qURuweVDbonIm3D5kDdF5C5cPuSuiNyDy4fc00jETkjnfnwTLh++KXK+D5cPeV9EPoDLh3wgIg/g8iEPRORXcPmQX4nIG3D5kDdE5G24fMjbIrIFlw/ZEpH7cPmQ+yJyGy4fclsjy2fqBK6U6AyFWXkNykUeaCkSqLkmynedrKMPez1gTvdKsPKsbsBfP7YRoNO4BLsdMO4OS7DyyLsJNtKPlW3RLVpNfNhbInYHRoAfuyNiv1AvSrBfBMy0lyVYea7tQjs/Vra+d+DOj70jYu9CyY+V16h7UOPH3gtYMcYl2D0Re1+9KsGGWP1JCVa2+02wK36svE61oL0fG2JNZyVY2Z4egAfjx8qr1UOo9WMfithH6k0J9pGI/RKsux/7ZcAK+7YEa9bYs7SCDMgfiWHGVlHr5LMSS2Og1hH4J/nakpBv3IV6CTPIMQPCHImImzniZiBiN0fsBsuV5XY0I39X5tLMEc1ARDdfm7A0Fdv38/ZYSgIQjRzRWEJUeaT4rk1f5uRdmBoJOc1XLiyF9CnN7TeWYj0eqi2vQdwrIHhsP6eRv07REkZQqKkqas/zNZ6REd1XIV5T9GZ6aXjIuGluFVzUGxHV9aC6IurYgzoWUTMPaiai5h7UXETZme/i2gEjwOof38WC7ngEsI9cfkXgFVyDVecWzNEIxs8eeIEPqOYe/G1S7C1dVZJhNI/rJGY5nhQs8QRKC7UG9TYqbFB8ndAMi0EybnlPx/h4h7mNhZ5zbIVP8pU8yjMm4XSGJM8gp4PeYkTzqR6d21RzQt4dl+rhb+Xz3pTq4bdJ4yfkxXOpHn6qpZ+eQvaWxrZOgW3CbBpr7dtyXRqcf2EapnyWVl20uPhWj/SYQXpvatLf0W9m5xTvZYtKrB9brkcjc/qXFfpXh4bVc+bouR4V9J7Y6zWlqHZPRjruteW6MqS0io60HPau7pvBNn39Zky5Ho098Li2KOZeOOW6o3ec98aW69E4UJz3PCFP3pTr0RjQPevDluvRwGxLR8f5tlzXsqMGOHa25bpWfURZYMwB8ZjnGusVTchPmmlqQ/IPqrM1rs+/uo5hzuZpHiNUU7K+bTmdbr6WVUtk/IUYrNq0phzoX8wcH6xIY6E2xfiKZZgW1vdVOnaNR83vghYjmP28ByDlzBOQ0OQk0HonQPGKGHUVe2ZwmyIOR8nhEqqta6eit2j5ctaoWPeMaqW4zPbW6rFN9jqjsTcmn3CXNCvpYbf0DZdRlDS0W9CQTK+O7t7q+VrU/oaIGy8hxvlI69GOEO+kVcepPq03HR1f0Ls8U7h4z8eOX8w2H2prgzFPSrYIZani6bYzeSS3DtfVdWVz3PwsojeK9mpOVmNIO1KZGIWabDF74wu6t7T3aU8OeTCNHrzHSFMZK941wyw65tMjsqiuvZV4o75Mho7LGVldY4+r0QMHPfCg68c4W7Bi3IVSC2KGfbhrBUQ5Z3NdpaTxifok3x1N6Q1WR/RJwUIaGmxv4oKFrIqynxeovAY0jgaO0sNpLNMx+PYKJTnq98ljY9ei5b9AO7dmf7tDY7x8NJdnYvrEdZO4RjRreFeX75Y5sAQL75NN8l+re4n86nBEGypxfepwZr2MaMc/pgh2TJ5xQrNNmh3F1m5+avmJ4bSnzN457manZCEjsn8RrE8pjcmIftyzA2YHnS1CQjYyxO4Mc+/G5+sMxTFm/bih4lMNdrzFZMtmxN/QdWdXRmORIwZeB06WxrbRyS75gjFxnWjrbud29eqDSHtOwh0lTNGOlYvE/xL9Nj9mnKytjAjUML6BTNs63/tIKWZBHXVola+2QaatK+X5XIanWmq7/lmZzhcka1DEhfLgat0Hzj26Z144SiYkd7bShtfRqmwuUh4v6RF7e0hRPNv9gV6BUe51WiXXaM61aZQMYBRM8yjCtJWyyMt8q3kVqYfRzv4v1K2ui1pDipGyGVzWkJTfjylac6VMYFTz+H1Js8mv9clSq2o+IxqLR85cfge1H8NvI7e5D6PTLViF6zQGmIK9sxrhmmilRRiv6wVeZmQaWvbe8rNj0rRya04TX7N1szH2vDaVPRo1b3TWwpRPQ+OFQ+NFoA5btNdotWjqjSV6JsYWLb1bGcqvDrdWDcozkbLskRnUMEBKN5YKo9oXqcoxvkG9FWltiLQ6MFvd3QB3zocg/XN9eXa/y1f3SN0g36ZHHhjHL32apUPyuUxtdaTGFJDzVW1f3dnfphrk3iULipT5HCfOGN516tF1kkv6M72ypWTnrUUw55Ze6zbGxrap/KsV5BHNiYzmpUFcpRaxlt+VI1qySJcdnyOizH+HfCr2O6pjZre1fSdRwZ+w8SbPKsuLI4UR6V/KvO2sRK87TvwaUUw40951F2jVf8NIgTEmk+D3LDN6Q7jK8U4Ce7Rdsp+rdop38UaORJdJ6oX6LMDGcNRrx7o7tkyPTd9+AS1R6/at+1rI/JJgjhK/0+zodWhVO9I+6mLp/nS0OnqVK95X6WG2xNfqY0Zt3MjCRnlFTFt9GsyFJarHhTEhXOr1oo789SSvIzPvToVSNq0N5WKmgW3Mc4qXpHOgiPB5dxe93twloR/dFXpdwrrUuEaihNm4VOcHXEuLWaloKUJy66U1KXHWo7L1wvJwVw1rx9lSxmQFEyXlbri124d2IVqRszFMoaf4ZG9ZnOjS/BQu/B0pX5RoOIbkEJvg515TW2r7PZyKeKXLnNmMqAZtQn8pBu/ofhZbVOvolUPdpR/CIZzHEHQtST+kFbWu7ExZltylHk7/NVmDiYpF6W3L+n1wucg9WeVUpz9DsnByb4bKfJNTty+GQ0hPilzC+fD+htSLQ2W+barXB0Nd7kGRQx0e5jxD2Du3revzcjlV62uVSygPXgfMzovB4Q5gecxi24VYqInzRt4/B7QOhxXUzWrxv/bD8LGc6vMK5ZbRN2cvAt46t4t1Zhb94vpzxnILGc3lHMN5pnnvrNfk58f+X1TrTaVOb94/ffRL7RgwvBaK86GydIx3R5GVN5QK7g/4ZEjVf9Tfz8hfJbzKaZTJUYeS2a8op2ZayNTMl5e+3plnITJZOmUyFanZeKJJJ2O31I66AT9buQdY95Qof1PJfxHr/462D7WHZD1MNp0zCG2qiykLYnfT+nRvz9GWSYxnevmMbwtqcE98l2rxvO9dao9nfluFvpV/ScJz/Y5KVb8QmSzv8tl51YUeFHfgOBdkvveN6Ew9Z7P4BNpRwB4jn6PiSMl8/bwgRJ/iwmVJF4Qwo6WKctdLuUtnkuIS2t1C33o0wsd6px/3HfB8fifPLkXql1TX0asDrtSSVHseqR5TZqBL+t+ACO3Xah3+ruuyX9K9FUkzegdFid44z6pPgp14x4X9mvEC5cFMpm6u26UU1dvdw+pMbKOUC594r8YPKvADR8omva2XFHdPVHXucFZBc6ZlcvdzR8rkPVkPGM128vFRHT/PK3jNA/p/uxR925H0JsjSpWx7RPt5E6KXaN1sk/R8rrI6b3urQlrz1SbTtCcr7TgwZySr9wQSPe7KZz+fg5RyNXEJHXeu84lM6bTI0EtJnp/jgNMQnYDeyn0N6alEZSZKMgv4EnkeIMs8gM6hIM2hSGEgSqLtw7Nza1eW/6+P1cLB5uUrv7l89f7m2ufX9f8D8qH6ifqpughr32/V5zD+99Q+cPq9+qP6i/pr413jD40/Nf7MTT84ozE/UoV/jb/9F2KaRrU=
    `(y, y0) = (y y0)2
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    yi
    2 { 1, 1}
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    Classification:
    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