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<!DOCTYPE html><html lang="en-US"><head><title>Introduction au Machine Learning</title><meta property="og:title" content="Introduction au Machine Learning"><meta name="author" content="Quentin Suire"><meta property="article:author" content="Quentin Suire"><meta name="description" content="Présentation d'introduction au ML faite pour le meetup Dat'apéro à Angers le 09/11/2023"><meta property="og:description" content="Présentation d'introduction au ML faite pour le meetup Dat'apéro à Angers le 09/11/2023"><meta charset="UTF-8"><meta name="viewport" content="width=device-width,height=device-height,initial-scale=1.0"><meta name="apple-mobile-web-app-capable" content="yes"><meta http-equiv="X-UA-Compatible" content="ie=edge"><meta property="og:type" content="website"><meta name="twitter:card" content="summary"><style>@media screen{body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=next] 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/*!
* Marp / Marpit Uncover theme
*
* @theme uncover
* @author Yuki Hattori
*
* @auto-scaling true
* @size 16:9 1280px 720px
* @size 4:3 960px 720px
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class="bespoke-marp-osc"><button data-bespoke-marp-osc="prev" tabindex="-1" title="Previous slide">Previous slide</button><span data-bespoke-marp-osc="page"></span><button data-bespoke-marp-osc="next" tabindex="-1" title="Next slide">Next slide</button><button data-bespoke-marp-osc="fullscreen" tabindex="-1" title="Toggle fullscreen (f)">Toggle fullscreen</button><button data-bespoke-marp-osc="presenter" tabindex="-1" title="Open presenter view (p)">Open presenter view</button></div><div id=":$p"><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="1" data-paginate="true" data-theme="uncover" lang="en-US" data-marpit-pagination="1" style="--paginate:true;--theme:uncover;" data-marpit-pagination-total="45">
<h1 id="introduction-au-machine-learning">Introduction au Machine Learning</h1>
</section>
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<h2 id="pr%C3%A9sentation">Présentation</h2>
<ul>
<li>Le machine learning ???</li>
<li>Comment ça marche ?</li>
<li>La mise en oeuvre</li>
<li>Quelques modèles</li>
</ul>
</section>
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<h2 id="le-machine-learning">Le machine learning ???</h2>
</section>
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<h3 id="vocabulaire">Vocabulaire</h3>
<ul>
<li>artificial intelligence (AI)</li>
<li>machine learning (ML)</li>
<li>deep learning (DL)</li>
</ul>
</section>
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<h3 id="d%C3%A9finition">Définition</h3>
<p>Le but du ML est de <strong>prendre des décisions</strong> ou <strong>faire des prédictions</strong> en se basant sur les <strong>données</strong>.</p>
</section>
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<p>Les <strong>données</strong> sont <strong>primordiales</strong>.</p>
</section>
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<h4 id="prendre-des-d%C3%A9cisions">Prendre des décisions</h4>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="8" data-paginate="true" data-theme="uncover" lang="en-US" data-marpit-pagination="8" style="--paginate:true;--theme:uncover;" data-marpit-pagination-total="45">
<h4 id="faire-des-pr%C3%A9dictions">Faire des prédictions</h4>
</section>
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<h3 id="applications">Applications</h3>
<ul>
<li>La recommandation de musique</li>
<li>Le filtrage des mails</li>
<li>Les feeds sur les réseaux sociaux</li>
<li>Les assistants vocaux</li>
<li>Les assistants type ChatGPT</li>
<li>Les voitures autonomes</li>
<li>...</li>
</ul>
</section>
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<h3 id="les-principaux-types">Les principaux types</h3>
<ul>
<li>apprentissage supervisé</li>
<li>apprentissage non-supervisé</li>
<li>apprentissage par renforcement</li>
</ul>
</section>
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<h4 id="lapprentissage-supervis%C3%A9">L'apprentissage supervisé</h4>
<p>Trouver une fonction <mjx-container class="MathJax" jax="SVG"><svg style="vertical-align: -0.566ex;" xmlns="http://www.w3.org/2000/svg" width="4.932ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 2180 1000"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><path data-c="1D453" d="M118 -162Q120 -162 124 -164T135 -167T147 -168Q160 -168 171 -155T187 -126Q197 -99 221 27T267 267T289 382V385H242Q195 385 192 387Q188 390 188 397L195 425Q197 430 203 430T250 431Q298 431 298 432Q298 434 307 482T319 540Q356 705 465 705Q502 703 526 683T550 630Q550 594 529 578T487 561Q443 561 443 603Q443 622 454 636T478 657L487 662Q471 668 457 668Q445 668 434 658T419 630Q412 601 403 552T387 469T380 433Q380 431 435 431Q480 431 487 430T498 424Q499 420 496 407T491 391Q489 386 482 386T428 385H372L349 263Q301 15 282 -47Q255 -132 212 -173Q175 -205 139 -205Q107 -205 81 -186T55 -132Q55 -95 76 -78T118 -61Q162 -61 162 -103Q162 -122 151 -136T127 -157L118 -162Z"></path></g><g data-mml-node="mo" transform="translate(550,0)"><path data-c="28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path></g><g data-mml-node="mi" transform="translate(939,0)"><path data-c="1D44B" d="M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z"></path></g><g data-mml-node="mo" transform="translate(1791,0)"><path data-c="29" d="M60 749L64 750Q69 750 74 750H86L114 726Q208 641 251 514T294 250Q294 182 284 119T261 12T224 -76T186 -143T145 -194T113 -227T90 -246Q87 -249 86 -250H74Q66 -250 63 -250T58 -247T55 -238Q56 -237 66 -225Q221 -64 221 250T66 725Q56 737 55 738Q55 746 60 749Z"></path></g></g></g></svg></mjx-container> telle que:</p>
<p><mjx-container class="MathJax" jax="SVG" display="true" style="margin: 0;"><svg style="vertical-align: -0.566ex; display: block; width: 100%; height: auto; max-height: 2.262ex;" xmlns="http://www.w3.org/2000/svg" width="9.675ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 4276.6 1000"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><path data-c="1D453" d="M118 -162Q120 -162 124 -164T135 -167T147 -168Q160 -168 171 -155T187 -126Q197 -99 221 27T267 267T289 382V385H242Q195 385 192 387Q188 390 188 397L195 425Q197 430 203 430T250 431Q298 431 298 432Q298 434 307 482T319 540Q356 705 465 705Q502 703 526 683T550 630Q550 594 529 578T487 561Q443 561 443 603Q443 622 454 636T478 657L487 662Q471 668 457 668Q445 668 434 658T419 630Q412 601 403 552T387 469T380 433Q380 431 435 431Q480 431 487 430T498 424Q499 420 496 407T491 391Q489 386 482 386T428 385H372L349 263Q301 15 282 -47Q255 -132 212 -173Q175 -205 139 -205Q107 -205 81 -186T55 -132Q55 -95 76 -78T118 -61Q162 -61 162 -103Q162 -122 151 -136T127 -157L118 -162Z"></path></g><g data-mml-node="mo" transform="translate(550,0)"><path data-c="28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path></g><g data-mml-node="mi" transform="translate(939,0)"><path data-c="1D44B" d="M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z"></path></g><g data-mml-node="mo" transform="translate(1791,0)"><path data-c="29" d="M60 749L64 750Q69 750 74 750H86L114 726Q208 641 251 514T294 250Q294 182 284 119T261 12T224 -76T186 -143T145 -194T113 -227T90 -246Q87 -249 86 -250H74Q66 -250 63 -250T58 -247T55 -238Q56 -237 66 -225Q221 -64 221 250T66 725Q56 737 55 738Q55 746 60 749Z"></path></g><g data-mml-node="mo" transform="translate(2457.8,0)"><path data-c="3D" d="M56 347Q56 360 70 367H707Q722 359 722 347Q722 336 708 328L390 327H72Q56 332 56 347ZM56 153Q56 168 72 173H708Q722 163 722 153Q722 140 707 133H70Q56 140 56 153Z"></path></g><g data-mml-node="mi" transform="translate(3513.6,0)"><path data-c="1D44C" d="M66 637Q54 637 49 637T39 638T32 641T30 647T33 664T42 682Q44 683 56 683Q104 680 165 680Q288 680 306 683H316Q322 677 322 674T320 656Q316 643 310 637H298Q242 637 242 624Q242 619 292 477T343 333L346 336Q350 340 358 349T379 373T411 410T454 461Q546 568 561 587T577 618Q577 634 545 637Q528 637 528 647Q528 649 530 661Q533 676 535 679T549 683Q551 683 578 682T657 680Q684 680 713 681T746 682Q763 682 763 673Q763 669 760 657T755 643Q753 637 734 637Q662 632 617 587Q608 578 477 424L348 273L322 169Q295 62 295 57Q295 46 363 46Q379 46 384 45T390 35Q390 33 388 23Q384 6 382 4T366 1Q361 1 324 1T232 2Q170 2 138 2T102 1Q84 1 84 9Q84 14 87 24Q88 27 89 30T90 35T91 39T93 42T96 44T101 45T107 45T116 46T129 46Q168 47 180 50T198 63Q201 68 227 171L252 274L129 623Q128 624 127 625T125 627T122 629T118 631T113 633T105 634T96 635T83 636T66 637Z"></path></g></g></g></svg></mjx-container></p><p>avec <mjx-container class="MathJax" jax="SVG"><svg style="vertical-align: 0;" xmlns="http://www.w3.org/2000/svg" width="1.928ex" height="1.545ex" role="img" focusable="false" viewBox="0 -683 852 683"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><path data-c="1D44B" d="M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z"></path></g></g></g></svg></mjx-container> : les exemples<br />
avec <mjx-container class="MathJax" jax="SVG"><svg style="vertical-align: 0;" xmlns="http://www.w3.org/2000/svg" width="1.726ex" height="1.545ex" role="img" focusable="false" viewBox="0 -683 763 683"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><path data-c="1D44C" d="M66 637Q54 637 49 637T39 638T32 641T30 647T33 664T42 682Q44 683 56 683Q104 680 165 680Q288 680 306 683H316Q322 677 322 674T320 656Q316 643 310 637H298Q242 637 242 624Q242 619 292 477T343 333L346 336Q350 340 358 349T379 373T411 410T454 461Q546 568 561 587T577 618Q577 634 545 637Q528 637 528 647Q528 649 530 661Q533 676 535 679T549 683Q551 683 578 682T657 680Q684 680 713 681T746 682Q763 682 763 673Q763 669 760 657T755 643Q753 637 734 637Q662 632 617 587Q608 578 477 424L348 273L322 169Q295 62 295 57Q295 46 363 46Q379 46 384 45T390 35Q390 33 388 23Q384 6 382 4T366 1Q361 1 324 1T232 2Q170 2 138 2T102 1Q84 1 84 9Q84 14 87 24Q88 27 89 30T90 35T91 39T93 42T96 44T101 45T107 45T116 46T129 46Q168 47 180 50T198 63Q201 68 227 171L252 274L129 623Q128 624 127 625T125 627T122 629T118 631T113 633T105 634T96 635T83 636T66 637Z"></path></g></g></g></svg></mjx-container> : les étiquettes</p>
</section>
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<p><img src="assets/supervised-cats-n-dogs.jpg" alt="" style="height:600px;" /></p>
</section>
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</section>
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<p><img src="assets/f_2cv.png" alt="f(2cv) = dog" /></p>
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<h4 id="lapprentissage-non-supervis%C3%A9">L'apprentissage non-supervisé</h4>
<p>Trouver des patterns ou des structures cachés dans des données.</p>
</section>
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<p><img src="assets/nolabels.png" alt="" style="height:600px;" /></p>
</section>
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<p><img src="assets/kmeans.png" alt="" style="height:600px;" /></p>
</section>
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<p><img src="assets/nmf.png" alt="" style="height:600px;" /></p>
</section>
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<h4 id="lapprentissage-par-renforcement">L'apprentissage par renforcement</h4>
<p>Faire évoluer un agent dans un environnement afin qu'il apprenne à réaliser des actions qui le récompensent.</p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="25" data-paginate="true" data-theme="uncover" lang="en-US" data-marpit-pagination="25" style="--paginate:true;--theme:uncover;" data-marpit-pagination-total="45">
<p><img src="assets/Reinforcement_learning_diagram.png" alt="" style="height:600px;" /></p>
</section>
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<p><img src="assets/deepmind_parkour.gif" alt="" style="width:1000px;" /></p>
</section>
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<p><img src="assets/mario.gif" alt="" style="height:600px;" /></p>
</section>
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<h2 id="comment-%C3%A7a-marche">Comment ça marche ?</h2>
</section>
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<h3 id="lentrainement-dun-mod%C3%A8le">L'entrainement d'un modèle</h3>
<ol start="0">
<li>On initialise un modèle</li>
<li>On utilise le modèle pour faire une prédiction</li>
<li>On compare la prédiction à ce qu'on attend</li>
<li>On corrige le modèle</li>
<li>On recommence à partir de 1 jusqu'à être satisfait</li>
</ol>
</section>
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<p><img src="assets/Linear_regression.png" alt="" style="width:800px;" /></p>
</section>
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<h3 id="d%C3%A9finir-un-crit%C3%A8re-d%C3%A9valuation-de-lerreur">Définir un critère d'évaluation de l'erreur</h3>
<ul>
<li>Distance L1, L2</li>
<li>Précision, rappel, score F1</li>
<li>Distance de Levenshtein</li>
<li>...</li>
</ul>
</section>
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<p><img src="assets/manhattan.png" alt="" style="height:600px;" /></p>
</section>
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<h3 id="%C3%A9viter-le-sous-apprentissage-et-le-sur-apprentissage">Éviter le sous-apprentissage et le sur-apprentissage</h3>
<p>Un modèle de ML doit être capable d'<strong>estimer</strong> et de <strong>généraliser</strong>.</p>
</section>
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<p><img src="assets/underfitting-overfitting.svg" alt="" style="width:1000px;" /></p>
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<h3 id="la-mise-en-oeuvre">La mise en oeuvre</h3>
</section>
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<p><img src="assets/RoadToDataScientist1.png" alt="" style="width:800px;" /></p>
</section>
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<p><img src="assets/datascience.png" alt="" style="height:600px;" /></p>
</section>
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<h4 id="cadrer-le-probl%C3%A8me">Cadrer le problème</h4>
<ul>
<li>identifier le type du problème</li>
<li>poser des hypothèses sur les données</li>
<li>choisir un critère d'évaluation</li>
<li>choisir un modèle</li>
<li>choisir un algorithme pour l'apprentissage</li>
<li>choisir un algorithme pour l'inférence</li>
</ul>
</section>
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<h2 id="quelques-mod%C3%A8les">Quelques modèles</h2>
</section>
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<h3 id="les-arbres-de-d%C3%A9cision">Les arbres de décision</h3>
<p><img src="assets/binary_decision_tree.png" alt="" style="width:800px;" /></p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="41" data-paginate="true" data-theme="uncover" lang="en-US" data-marpit-pagination="41" style="--paginate:true;--theme:uncover;" data-marpit-pagination-total="45">
<h3 id="les-for%C3%AAts-al%C3%A9atoires">Les forêts aléatoires</h3>
<p>Un ensemble d'arbres de décision</p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="42" data-paginate="true" data-theme="uncover" lang="en-US" data-marpit-pagination="42" style="--paginate:true;--theme:uncover;" data-marpit-pagination-total="45">
<h3 id="les-r%C3%A9seaux-de-neurones">Les réseaux de neurones</h3>
<p><img src="assets/ann.webp" alt="" style="height:600px;" /></p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="43" data-paginate="true" data-theme="uncover" lang="en-US" data-marpit-pagination="43" style="--paginate:true;--theme:uncover;" data-marpit-pagination-total="45">
<h3 id="les-knn">Les KNN</h3>
<p><img src="assets/knn.png" alt="" style="width:700px;" /></p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="44" data-paginate="true" data-theme="uncover" lang="en-US" data-marpit-pagination="44" style="--paginate:true;--theme:uncover;" data-marpit-pagination-total="45">
<h2 id="conclusion">Conclusion</h2>
<p>Le Machine Learning vise à faire des prédictions ou prendre des décisions en se basant sur des données.</p>
<p>Il faut :</p>
<ul>
<li>identifier le type du problème</li>
<li>comprendre les données</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="45" data-paginate="true" data-theme="uncover" lang="en-US" data-marpit-pagination="45" style="--paginate:true;--theme:uncover;" data-marpit-pagination-total="45">
<h2 id="ressources-pour-aller-plus-loin">Ressources pour aller plus loin :</h2>
<ul>
<li>
<p><a href="https://fr.wikipedia.org/wiki/Apprentissage_automatique">https://fr.wikipedia.org/wiki/Apprentissage_automatique</a></p>
</li>
<li>
<p><a href="https://scikit-learn.org/stable/user_guide.html">https://scikit-learn.org/stable/user_guide.html</a></p>
</li>
<li>
<p>cours en accès libre :</p>
<ul>
<li><img class="emoji" draggable="false" alt="⭐" src="https://cdn.jsdelivr.net/gh/jdecked/[email protected]/assets/svg/2b50.svg" data-marp-twemoji=""/> <a href="https://www.kaggle.com/learn">https://www.kaggle.com/learn</a></li>
<li><a href="https://www.coursera.org/specializations/machine-learning-introduction">https://www.coursera.org/specializations/machine-learning-introduction</a></li>
<li><a href="https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/courseware/Week1/intro_ml">https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/courseware/Week1/intro_ml</a></li>
</ul>
</li>
</ul>
</section>
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</script></foreignObject></svg></div><div class="bespoke-marp-note" data-index="3" tabindex="0"><p>IA > ML > DL > generative models</p><p>AGI vs ANI vs ASI</p><p>modèles génératifs</p></div><div class="bespoke-marp-note" data-index="4" tabindex="0"><p>prendre des décisions = faire une classification</p><p>en se basant sur les données -> en "apprenant"</p><p>en économie et en psychologie, on cherche à trouver des modèles des processus sous-jacents</p><p>en statistique, on cherche un modèle qui correspond aux données</p></div><div class="bespoke-marp-note" data-index="6" tabindex="0"><p>le commentaire est-il positif ou négatif ?</p><p>la transaction est-elle frauduleuse ?</p><p>qui essaye de déverrouiller le téléphone ?</p><p>dois-je tourner à gauche, à droite, accélérer, freiner ?</p></div><div class="bespoke-marp-note" data-index="7" tabindex="0"><p>est-ce qu'il va pleuvoir dans l'heure ?</p><p>quel produit va aimer tel utilisateur ?</p></div><div class="bespoke-marp-note" data-index="10" tabindex="0"><p>modèles de classification ou de regression (avec Y dans les réels)</p><p>on connait les classes Y (étiquettes), on connait également les exemples X (caractéristiques)</p></div><div class="bespoke-marp-note" data-index="18" tabindex="0"><p>faire de la réduction de dimensions</p><p>identifier des groupes dans un ensemble d'utilisateurs (clustering)</p><p>détection d'anomalies</p></div><div class="bespoke-marp-note" data-index="23" tabindex="0"><p>robot qui doit apprendre à marcher</p><p>IA qui joue à Mario</p></div><div class="bespoke-marp-note" data-index="29" tabindex="0"><p>erreur = somme des carrés des résidus</p></div><div class="bespoke-marp-note" data-index="30" tabindex="0"><p>La distance de Levenshtein est une distance, au sens mathématique du terme, donnant une mesure de la différence entre deux chaînes de caractères. Elle est égale au nombre minimal de caractères qu'il faut supprimer, insérer ou remplacer pour passer d'une chaîne à l'autre.</p><p>L(chien, chat) = 3</p><p>distance euclidienne (à vol d'oiseau)</p><p>distance de manhattan</p></div><div class="bespoke-marp-note" data-index="32" tabindex="0"><p>la séparation en plusieurs ensembles : train, validation, test</p></div><div class="bespoke-marp-note" data-index="35" tabindex="0"><p>http://nirvacana.com/thoughts/2013/07/08/becoming-a-data-scientist/</p></div><div class="bespoke-marp-note" data-index="37" tabindex="0"><p>nettoyer les données</p><p>arbre de décision, forêt aléatoire, régression logistique, réseau de neurone, SVM, K-plus-proches-voisins</p></div><div class="bespoke-marp-note" data-index="38" tabindex="0"><p>https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py</p></div><script>/*!! License: https://unpkg.com/@marp-team/[email protected]/lib/bespoke.js.LICENSE.txt */
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