{"id":1419,"date":"2020-02-03T19:36:33","date_gmt":"2020-02-03T19:36:33","guid":{"rendered":"https:\/\/eic.cefet-rj.br\/~ebezerra\/?page_id=1419"},"modified":"2020-10-19T19:33:43","modified_gmt":"2020-10-19T19:33:43","slug":"ml_2020","status":"publish","type":"page","link":"https:\/\/eic.cefet-rj.br\/~ebezerra\/ml_2020\/","title":{"rendered":"Machine Learning (2020)"},"content":{"rendered":"<p>&nbsp;<\/p>\n<figure id=\"attachment_144\" aria-describedby=\"caption-attachment-144\" style=\"width: 453px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/www.kdnuggets.com\/images\/cartoon-machine-learning-class.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-144\" src=\"https:\/\/www.kdnuggets.com\/images\/cartoon-machine-learning-class.jpg\" alt=\"\" width=\"453\" height=\"337\" \/><\/a><figcaption id=\"caption-attachment-144\" class=\"wp-caption-text\">&#8220;Teacher: Robbie, stop misbehaving or I will send you back to data cleaning.&#8221;<\/figcaption><\/figure>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<h3>Local\/hor\u00e1rio<\/h3>\n<ul>\n<li>CEFET\/RJ, por videoconfer\u00eancia (plataforma MS Teams)<\/li>\n<li>Dia\/hor\u00e1rio: 5as-feiras, das 13:25h \u00e0s 17:00h<\/li>\n<\/ul>\n<hr \/>\n<h3>Overview<\/h3>\n<p>Aprendizado de M\u00e1quina (<em>Machine Learning<\/em>) \u00e9 um campo de estudo da Intelig\u00eancia Artificial cujo objeto de estudo s\u00e3o sistemas que podem aprender a realizar alguma tarefa por meio de experi\u00eancias. Neste curso, o objetivo \u00e9 apresentar uma introdu\u00e7\u00e3o aos conceitos, modelos, m\u00e9todos, t\u00e9cnicas e aplica\u00e7\u00f5es do Aprendizado de M\u00e1quina. S\u00e3o tamb\u00e9m apresentados alguns algoritmos pertencentes a diferentes fam\u00edlias de m\u00e9todos em AM (simbolistas, conexionistas, probabil\u00edsticos, baseados em proximidade).<\/p>\n<hr size=\"4\" width=\"100%\" \/>\n<h3>Plano do curso<\/h3>\n<p>Veja o <a href=\"https:\/\/www.dropbox.com\/s\/30ck5ha63jhgyrq\/AM-PlanoCurso%20%282020%29.xlsx?dl=0\">plano do curso<\/a>. Veja tamb\u00e9m o <a href=\"http:\/\/dippg.cefet-rj.br\/attachments\/article\/247\/DIPPG_2020_CalendarioAcademico.pdf\">calend\u00e1rio acad\u00eamico das p\u00f3s-gradua\u00e7\u00f5es<\/a> do CEFET\/RJ. Veja ainda a <a href=\"http:\/\/eic.cefet-rj.br\/~ebezerra\/am-complementar\/\">p\u00e1gina<\/a> com material relevante organizado por aula.<\/p>\n<table style=\"border: 1px solid #cccccc;\">\n<tbody>\n<tr>\n<td style=\"border: 1px solid #cccccc;\"><b>Class<\/b><\/td>\n<td style=\"border: 1px solid #cccccc;\"><b>Date<\/b><\/td>\n<td style=\"border: 1px solid #cccccc;\"><b>Lectures<\/b><\/td>\n<td style=\"border: 1px solid #cccccc;\"><b>Readings<\/b><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">1<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">20\/fev<\/td>\n<td style=\"border: 1px solid #cccccc;\">Vis\u00e3o geral do curso (<a href=\"https:\/\/www.dropbox.com\/s\/1hok0rk9jzd5wze\/AM%2000%20%28Apresenta%C3%A7%C3%A3o%20do%20curso%29.pptx?dl=0\">AM00<\/a>)<br \/>\nVis\u00e3o geral do AM (<a href=\"https:\/\/www.dropbox.com\/s\/uclepio9yl0hpwe\/AM%2001%20%28Vis%C3%A3o%20geral%20do%20AM%29.pptx?dl=0\">AM01<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc;\">Mitchell, Cap. 1; (<a href=\"http:\/\/www.cs.cmu.edu\/%7Etom\/mlbook\/keyIdeas.pdf\">r1<\/a>); (<a href=\"http:\/\/homes.cs.washington.edu\/~pedrod\/papers\/cacm12.pdf\">r2<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a02<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">17\/set<\/td>\n<td style=\"border: 1px solid #cccccc;\">Regress\u00e3o linear com uma vari\u00e1vel (<a href=\"https:\/\/www.dropbox.com\/s\/c56g2qbta1tk0fz\/AM%2002%20%28Regress%C3%A3o%20Linear%20com%20Uma%20Vari%C3%A1vel%29.pptx?dl=0\">AM02<\/a>)<br \/>\nRegress\u00e3o linear com v\u00e1rias vari\u00e1veis (<a href=\"https:\/\/www.dropbox.com\/s\/q09jf87p1apk8a4\/AM%2003%20%28Regress%C3%A3o%20Linear%20Multivariada%29.pptx?dl=0\">AM03<\/a>)<br \/>\nRegress\u00e3o log\u00edstica (<a href=\"https:\/\/www.dropbox.com\/s\/2pp9e0mogtalfrq\/AM%2004%20%28Regress%C3%A3o%20Log%C3%ADstica%29.pptx?dl=0\">AM04<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc;\"><a href=\"http:\/\/adit.io\/posts\/2016-03-13-Logistic-Regression.html\">regress\u00e3o log\u00edstica<\/a>; <a href=\"http:\/\/adit.io\/posts\/2016-02-20-Linear-Regression-in-Pictures.html\">regress\u00e3o linear<\/a>; <a href=\"http:\/\/cs229.stanford.edu\/notes\/cs229-notes1.pdf\">cs229-notes1<\/a><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a03<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">24\/set<\/td>\n<td style=\"border: 1px solid #cccccc;\">kNN (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_knn.ipynb\">AM05<\/a>)<br \/>\nDecision Trees (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_dtree.ipynb\">AM06<\/a>)<br \/>\nModel Evaluation (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_modeleval_sup.ipynb\">AM07<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc;\">(<a href=\"https:\/\/www.dropbox.com\/s\/cpmncilgkmsv6j0\/1811.12808.pdf?dl=0\">r3<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a04<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a001\/out<\/td>\n<td style=\"border: 1px solid #cccccc;\">Model Selection (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_modelselection.ipynb\">AM08<\/a>)<br \/>\nNaive Bayes Classifier (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_naivebayes.ipynb\">AM09<\/a>)<br \/>\nDimensionality Reduction (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ml_ppcic_dim_reduc.ipynb\">AM10<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc;\">(<a href=\"http:\/\/robotics.stanford.edu\/~ronnyk\/vote.pdf\">r4<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">5<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">08\/out<\/td>\n<td style=\"border: 1px solid #cccccc;\">Tutorial Keras (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_ann_keras.ipynb\">AM11<\/a>)<\/p>\n<p>Redes Neurais &#8211; Basics (<a href=\"https:\/\/www.dropbox.com\/s\/x4diyavmwp4kwvd\/AM%2012%20%28ANN%20basics%29.pptx?dl=0\">AM12<\/a>)<br \/>\nRedes neurais &#8211; backprop (<a href=\"https:\/\/www.dropbox.com\/s\/2s991qioeh7rbzf\/AM%2013%20%28ANNs%20-%20backprop%29.pptx?dl=0\">AM13<\/a>)<br \/>\nRedes neurais &#8211; activation functions (<a href=\"https:\/\/www.dropbox.com\/s\/ueg2gpjf811616d\/AM%2014%20%28ANNs%20-%20activation%20functions%29.pptx?dl=0\">AM14<\/a>)<br \/>\nRedes neurais &#8211; regularization (<a href=\"https:\/\/www.dropbox.com\/s\/j43g20s0qliduq7\/AM%2015%20%28ANNs%20-%20regularization%29.pptx?dl=0\">AM15<\/a>)<br \/>\nRedes neurais &#8211; misc topics (<a href=\"https:\/\/www.dropbox.com\/s\/0yyhnzg5ddum2fe\/AM%2016%20%28ANNs%20-%20learning%20-%20misc%29.pptx?dl=0\">AM16<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc;\">(<a href=\"https:\/\/datajobs.com\/data-science-repo\/Recommender-Systems-[Netflix].pdf\">r5<\/a>) (<a href=\"https:\/\/courses.cs.washington.edu\/courses\/csep546\/17au\/psetwww\/2\/algsweb.pdf\">r6<\/a>)<\/p>\n<p><a href=\"https:\/\/youtu.be\/etebnvblYLY\">Redes Neurais Profundas: O que s\u00e3o? Como vivem? De que se alimentam?<\/a><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a06<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\"><strong>EXTRA<\/strong><\/td>\n<td style=\"border: 1px solid #cccccc;\"><strong><strong>(Ricardo Sant&#8217;Ana,\u00a0<\/strong><\/strong><strong>02\/out, 18:00h-20:30h)<\/strong>: <a href=\"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2020\/02\/Aula-Ensemble-Learning.pdf\">Ensemble Learning<\/a>, <a href=\"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2020\/02\/papers_ensemble_learning.zip\">papers relacionados<\/a>, <a href=\"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2020\/02\/AulaEnsembleLearning.zip\">c\u00f3digo<\/a><\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a07<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\"><strong>EXTRA<\/strong><\/td>\n<td style=\"border: 1px solid #cccccc;\"><strong>(Rafaela Castro, 13\/out, 18:00h-20:00h)<\/strong>: <a href=\"https:\/\/docs.google.com\/presentation\/d\/1ZdoYq7p34ROXDEY5yzj-pqlPOqL1RFq2FO5GjomF3y0\/edit?usp=sharing\">convnets<\/a>; <a href=\"https:\/\/docs.google.com\/presentation\/d\/1vz6vLXgtbGVqUSQH-kX8Vg4bEiEClSfo1OdFfyF0IXc\/edit?usp=sharing\">Tutorial PyTorch<\/a>, <a href=\"https:\/\/github.com\/rafaela00castro\/pytorch-hands-on\/blob\/master\/mnist_cnn.ipynb\">c\u00f3digo<\/a><\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a08<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\"><strong>EXTRA<\/strong><\/td>\n<td style=\"border: 1px solid #cccccc;\"><strong>(Alan Fontoura, 16\/out, 18:00h-20:00h)<\/strong>: Aprendizado por Refor\u00e7o; Tutorial Open AI Gym<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><a href=\"https:\/\/docs.google.com\/forms\/d\/e\/1FAIpQLSdBNCuX1mBJ6HpXggzyXMGK5AsfL1O3oLnJvmOheP8LV2zt4A\/viewform\">Formul\u00e1rio de avalia\u00e7\u00e3o<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr size=\"4\" width=\"100%\" \/>\n<h3>Projects<\/h3>\n<ul>\n<li><a href=\"https:\/\/www.dropbox.com\/s\/sk2wbem8s3wic2o\/am-2019-trabalho01.pdf?dl=0\">T1<\/a> &#8211;\u00a0<span style=\"color: #ff0000;\">Due 24\/mar<\/span><\/li>\n<li><a href=\"https:\/\/www.dropbox.com\/s\/q0v5eirqmu5888z\/am-2020-T2.pdf?dl=0\">T2<\/a>\u00a0&#8211;\u00a0<span style=\"color: #ff0000;\">Due 11\/out<\/span><\/li>\n<li><a href=\"https:\/\/www.dropbox.com\/s\/heqwffpmtpiicwl\/am-2020-T3.pdf?dl=0\">T3<\/a>\u00a0&#8211; <span style=\"color: #ff0000;\">Due 25\/out<\/span><\/li>\n<\/ul>\n<hr size=\"4\" width=\"100%\" \/>\n<h3>Additional resources<\/h3>\n<ul>\n<li>Video: <a href=\"https:\/\/www.youtube.com\/watch?v=iX5V1WpxxkY\">Recurrent Neural Networks, Image Captioning, LSTM<\/a>, Andrej Karpathy.<\/li>\n<li>Online course: <a href=\"http:\/\/course.fast.ai\">Practical Deep Learning For Coders<\/a><\/li>\n<li>Online course: <a href=\"https:\/\/www.coursera.org\/learn\/neural-networks-deep-learning\">Neural Networks and Deep Learning<\/a><\/li>\n<li>Online course: <a href=\"https:\/\/www.codecademy.com\/learn\/learn-python\">(Codecademy) Learn Python<\/a><\/li>\n<li><a href=\"http:\/\/w4nderlu.st\/teaching\/word-embeddings\">Word Embeddings<\/a><\/li>\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=ZSDrM-tuOiA\">Representation Learning for Reading Comprehension<\/a><\/li>\n<li><a href=\"https:\/\/www.oreilly.com\/learning\/generative-adversarial-networks-for-beginners\">Practical Generative Adversarial Networks for Beginners<\/a><\/li>\n<\/ul>\n<hr size=\"4\" width=\"100%\" \/>\n<h3>Readings<\/h3>\n<ul>\n<li>(r1) Tom Mitchel,\u00a0<a href=\"http:\/\/www.cs.cmu.edu\/~tom\/mlbook\/keyIdeas.pdf\">Key Ideas in Machine Learning<\/a><\/li>\n<li>(r2) Pedro Domingos, <a href=\"https:\/\/homes.cs.washington.edu\/~pedrod\/papers\/cacm12.pdf\">A Few Useful Things to Know About Machine Learning<\/a><\/li>\n<li>(r3) Sebastian Raschka, <a href=\"https:\/\/arxiv.org\/abs\/1811.12808\">Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning<\/a><\/li>\n<li>(r4) Eric Bauer &amp; Ron Kohavi,\u00a0<a href=\"http:\/\/robotics.stanford.edu\/~ronnyk\/vote.pdf\">An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants<\/a><\/li>\n<li>(r5) <a href=\"https:\/\/datajobs.com\/data-science-repo\/Recommender-Systems-[Netflix].pdf\">Matriz Factorization for Recommender Systems<\/a><\/li>\n<li>(r6)\u00a0<a href=\"https:\/\/courses.cs.washington.edu\/courses\/csep546\/17au\/psetwww\/2\/algsweb.pdf\">Empirical Analysis of Predictive Algorithms for Collaborative Filtering<\/a><\/li>\n<\/ul>\n<h3>Books<\/h3>\n<ul>\n<li>Max Kuhn and Kjell Johnson, <a href=\"http:\/\/www.feat.engineering\">Feature Engineering and Selection: A Practical Approach for Predictive Models<\/a><\/li>\n<li>Jeremy Watt et al, <a href=\"https:\/\/github.com\/jermwatt\/machine_learning_refined\">Machine Learning Refined: Foundations, Algorithms, and Applications<\/a><\/li>\n<li>Aur\u00e9lien G\u00e9ron, <a href=\"https:\/\/www.oreilly.com\/library\/view\/hands-on-machine-learning\/9781492032632\/\">Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow<\/a>, 2nd Edition, 2019.<\/li>\n<li>Sebastian Raschka, <a href=\"https:\/\/www.amazon.com.br\/Python-Machine-Learning-scikit-learn-TensorFlow-ebook\/dp\/B0742K7HYF\/ref=dp_ob_image_def\">Python Machine Learning<\/a>, 3rd ed, Packt Publishing, 2019.<\/li>\n<li>Jake VanderPlas, <a href=\"https:\/\/jakevdp.github.io\/PythonDataScienceHandbook\/\">Python Data Science Handbook<\/a>, 2016.<\/li>\n<li>Christopher Bishop, <a href=\"https:\/\/www.springer.com\/br\/book\/9780387310732\">Pattern Recognition and Machine Learning<\/a>, Springer, 2006. <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2006\/01\/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf?fbclid=IwAR2P6672x2_opMCswcEQrR-W3jhlDPKda7xmOvC-uv1Y73ee-mQ_iHd0NgU\">pdf<\/a>\u00a0<a href=\"https:\/\/github.com\/ctgk\/PRML\">github<\/a><\/li>\n<li>Ethen Alpaydin, <a href=\"https:\/\/mitpress.mit.edu\/books\/introduction-machine-learning\">Introduction to Machine Learning<\/a>, MIT Press, 2010.<\/li>\n<li>Ian Goodfellow et al, <a href=\"http:\/\/www.deeplearningbook.org\/\">Deep Learning<\/a>, MIT Press, 2016.<\/li>\n<li>Tom Mitchell, <a href=\"http:\/\/www.cs.cmu.edu\/afs\/cs.cmu.edu\/user\/mitchell\/ftp\/mlbook.html\">Machine Learning<\/a>, McGraw-Hill, 1997.<\/li>\n<li>Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge, MA, 1 edition edition, August 2012.<\/li>\n<li>Peter Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data . Cambridge University Press, Cambridge ; New York, 1st edition, 2012.<\/li>\n<li>Christopher Bishop. Pattern Recognition and Machine Learning . Springer, New York, October 2007.<\/li>\n<li>Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning: with Applications in R . Springer, 1st ed. 2013. corr. 4th printing 2014<\/li>\n<li>Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning From Data . AMLBook, S.l., March 2012.<\/li>\n<li>Brett Lantz. Machine Learning with R . Packt Publishing, Birmingham, October 2013.<\/li>\n<li>Simon O. Haykin. Neural Networks and Learning Machines . Prentice Hall, New York, 3 edition edition, November 2008.<\/li>\n<li>Ani Adhikari &amp; John DeNero,\u00a0<a href=\"http:\/\/artint.info\/\" target=\"_blank\" rel=\"noopener\">Artificial Intelligence: Foundations of Computational Agents, second edition, Cambridge University Press, 2017<\/a>.<\/li>\n<\/ul>\n<p>Livros de interesse geral sobre Aprendizado de M\u00e1quina<\/p>\n<ul>\n<li>Gary Marcus &amp; Ernest Davis, <a href=\"https:\/\/www.goodreads.com\/book\/show\/43999120-rebooting-ai\">Rebooting AI:\u00a0Building Artificial Intelligence We Can Trust<\/a>, 2019.<\/li>\n<li>Melanie Mitchel, <a href=\"https:\/\/www.amazon.com\/Artificial-Intelligence-Guide-Thinking-Humans\/dp\/0374257833\">Artificial Intelligence: A Guide for Thinking Humans<\/a>, 2019.<\/li>\n<li>Jimmy Soni &amp; Rob Goodman, <a href=\"http:\/\/www.simonandschuster.com\/books\/A-Mind-at-Play\/Jimmy-Soni\/9781476766683\">A Mind at Play: How Claude Shannon Invented the Information Age<\/a>, 2017.<\/li>\n<li>Pedro Domingos,\u00a0<a href=\"https:\/\/www.amazon.com\/Master-Algorithm-Ultimate-Learning-Machine\/dp\/0465065708\/ref=pd_bxgy_14_img_2?_encoding=UTF8&amp;psc=1&amp;refRID=8ETNCQKT3X406VVJ3JV9\">The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World<\/a>, 2015.<\/li>\n<li>Nick Bostrom, <a href=\"https:\/\/www.amazon.com\/Superintelligence-Dangers-Strategies-Nick-Bostrom\/dp\/0198739834\">Superintelligence: Paths, Dangers, Strategies<\/a>, 2014.<\/li>\n<\/ul>\n<hr \/>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; &nbsp; &nbsp; Local\/hor\u00e1rio CEFET\/RJ, por videoconfer\u00eancia (plataforma MS Teams) Dia\/hor\u00e1rio: 5as-feiras, das 13:25h \u00e0s 17:00h Overview Aprendizado de M\u00e1quina (Machine Learning) \u00e9 um campo de estudo da Intelig\u00eancia Artificial cujo objeto de estudo s\u00e3o sistemas que podem aprender a realizar alguma tarefa por meio de experi\u00eancias. Neste curso, o objetivo \u00e9 apresentar uma introdu\u00e7\u00e3o [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1419","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/1419","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/comments?post=1419"}],"version-history":[{"count":31,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/1419\/revisions"}],"predecessor-version":[{"id":1512,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/1419\/revisions\/1512"}],"wp:attachment":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/media?parent=1419"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}