{"id":1156,"date":"2019-02-08T08:20:43","date_gmt":"2019-02-08T08:20:43","guid":{"rendered":"http:\/\/eic.cefet-rj.br\/~ebezerra\/?page_id=1156"},"modified":"2019-05-09T19:17:50","modified_gmt":"2019-05-09T19:17:50","slug":"ml2019","status":"publish","type":"page","link":"https:\/\/eic.cefet-rj.br\/~ebezerra\/ml2019\/","title":{"rendered":"Machine Learning (2019)"},"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>Cursos<\/h3>\n<ul>\n<li>Mestrado em Ci\u00eancia da Computa\u00e7\u00e3o<\/li>\n<\/ul>\n<h3>Local\/hor\u00e1rio<\/h3>\n<ul>\n<li>Bloco E, 5o andar, sala E-515<\/li>\n<li>Dia\/hor\u00e1rio: 5as-feiras, das 13:25h \u00e0s 17:00h<\/li>\n<\/ul>\n<hr \/>\n<h3>Apresenta\u00e7\u00e3o<\/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 \/>\n<h3>Livros<\/h3>\n<ul>\n<li>Christopher Bishop, <a href=\"https:\/\/www.springer.com\/br\/book\/9780387310732\">Pattern Recognition and Machine Learning<\/a>, Springer, 2006.<\/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>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>, 2nd ed, Packt Publishing, 2015.<\/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<\/ul>\n<hr size=\"4\" width=\"100%\" \/>\n<h3>Plano de curso<\/h3>\n<p>Veja o <a href=\"https:\/\/www.dropbox.com\/s\/wxn4y56e03mdnjs\/AM-PlanoCurso%20%282019%29.xlsx?dl=0\">plano do curso<\/a>. Veja tamb\u00e9m o <a href=\"http:\/\/dippg.cefet-rj.br\/attachments\/article\/247\/DIPPG_2019_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>Aula<\/b><\/td>\n<td style=\"border: 1px solid #cccccc;\"><b>Data<\/b><\/td>\n<td style=\"border: 1px solid #cccccc;\"><b>T\u00f3pico<\/b><\/td>\n<td style=\"border: 1px solid #cccccc;\"><b>Leitura<\/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;\">14\/fev<\/td>\n<td style=\"border: 1px solid #cccccc;\">Vis\u00e3o geral do curso (<a href=\"https:\/\/www.dropbox.com\/s\/pm2aghyhdbiiygg\/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\/33ddxmzzv6zi8j6\/AM%2001%20%28Vis%C3%A3o%20geral%20do%20AM%29.pptx?dl=0\">AM01<\/a>).<br \/>\nRegress\u00e3o linear com uma vari\u00e1vel (<a href=\"https:\/\/www.dropbox.com\/s\/1xojsso6qdz2269\/AM%2002%20%28Regress%C3%A3o%20Linear%20com%20Uma%20Vari%C3%A1vel%29.pptx?dl=0\">AM02<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc;\">Mitchell, Cap. 1; <a href=\"http:\/\/www.cs.cmu.edu\/%7Etom\/mlbook\/keyIdeas.pdf\">paper<\/a>;\u00a0<a href=\"http:\/\/homes.cs.washington.edu\/~pedrod\/papers\/cacm12.pdf\">paper<\/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;\">\u00a028\/fev<\/td>\n<td style=\"border: 1px solid #cccccc;\">Regress\u00e3o linear com v\u00e1rias vari\u00e1veis (<a href=\"https:\/\/www.dropbox.com\/s\/p3drhlb3vfrwkbc\/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\/3abfrf8lw1g8r85\/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;\">14\/mar<\/td>\n<td style=\"border: 1px solid #cccccc;\">Regulariza\u00e7\u00e3o, valida\u00e7\u00e3o cruzada (<a href=\"https:\/\/www.dropbox.com\/s\/rjmmy4je6sk6nj4\/AM%2005%20%28Regulariza%C3%A7%C3%A3o%29.pptx?dl=0\">AM05<\/a>)<br \/>\nSele\u00e7\u00e3o e avalia\u00e7\u00e3o de modelos de AM (<a href=\"https:\/\/www.dropbox.com\/s\/pxe18o2tqhnfsg6\/AM%2006%20%28Sele%C3%A7%C3%A3o%20e%20avalia%C3%A7%C3%A3o%20de%20modelos%20de%20AM%29.pptx?dl=0\">AM06<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc;\">\u00a0<a href=\"https:\/\/www.dropbox.com\/s\/cpmncilgkmsv6j0\/1811.12808.pdf?dl=0\">paper<\/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;\">21\/mar<\/td>\n<td style=\"border: 1px solid #cccccc;\">\u00c1rvores de Decis\u00e3o (<a href=\"https:\/\/www.dropbox.com\/s\/8ogfine3fkzfizm\/AM%2007%20%28%C3%81rvores%20de%20Decis%C3%A3o%29.pptx?dl=0\">AM_07<\/a>); Aprendizado de Comit\u00eas (<a href=\"https:\/\/www.dropbox.com\/s\/7vdmdi82wglicwy\/AM%2008%20%28Aprendizado%20de%20Comit%C3%AAs%29.pptx?dl=0\">AM08<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc;\"><a href=\"http:\/\/robotics.stanford.edu\/~ronnyk\/vote.pdf\">paper<\/a>; <a href=\"http:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2018\/05\/AM-ensembles.pdf\">ensembles.ipynb<\/a>; <a href=\"https:\/\/gist.github.com\/tomokishii\/8290b06cd908a9fb694b217e8e09a46b\">MLInP-Chap7.ipynb<\/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;\">\u00a028\/mar<\/td>\n<td style=\"border: 1px solid #cccccc;\">kNN (<a href=\"https:\/\/www.dropbox.com\/s\/82l0lawuee3pdl4\/AM%2009%20%28kNN%29.pptx?dl=0\">AM09<\/a>); Sistemas de Recomenda\u00e7\u00e3o (<a href=\"https:\/\/www.dropbox.com\/s\/ewi85ecnp0jp9rl\/AM%2010%20%28Sistemas%20de%20Recomenda%C3%A7%C3%A3o%29.pptx?dl=0\">AM10<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc;\"><a href=\"https:\/\/datajobs.com\/data-science-repo\/Recommender-Systems-[Netflix].pdf\">paper<\/a>;\u00a0<a href=\"https:\/\/courses.cs.washington.edu\/courses\/csep546\/17au\/psetwww\/2\/algsweb.pdf\">paper<\/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;\">\u00a004\/abr<\/td>\n<td style=\"border: 1px solid #cccccc;\">GD e variantes (<a href=\"https:\/\/www.dropbox.com\/s\/hr2uek79t14rhpx\/AM%2011%20%28GD%20e%20variantes%29.pptx?dl=0\">AM11<\/a>)<br \/>\nRedes Neurais &#8211; Basics (<a href=\"https:\/\/www.dropbox.com\/s\/x4diyavmwp4kwvd\/AM%2012%20%28ANN%20basics%29.pptx?dl=0\">AM12<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><a href=\"https:\/\/towardsdatascience.com\/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464\">link<\/a>; <a href=\"https:\/\/playground.tensorflow.org\/#activation=tanh&amp;batchSize=10&amp;dataset=circle&amp;regDataset=reg-plane&amp;learningRate=0.03&amp;regularizationRate=0&amp;noise=0&amp;networkShape=4,2&amp;seed=0.25436&amp;showTestData=false&amp;discretize=false&amp;percTrainData=50&amp;x=true&amp;y=true&amp;xTimesY=false&amp;xSquared=false&amp;ySquared=false&amp;cosX=false&amp;sinX=false&amp;cosY=false&amp;sinY=false&amp;collectStats=false&amp;problem=classification&amp;initZero=false&amp;hideText=false\">link<\/a>; <a href=\"http:\/\/colah.github.io\/posts\/2015-08-Backprop\/\">link<\/a>; <a href=\"http:\/\/www.iro.umontreal.ca\/~pift6266\/A06\/refs\/backprop_old.pdf\">paper<\/a><\/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;\">\u00a011\/abr<\/td>\n<td style=\"border: 1px solid #cccccc;\">Redes 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; text-align: left;\">\u00a0<a href=\"https:\/\/github.com\/ramonsilvanet\/getting-started-tf\">link<\/a>; <a href=\"https:\/\/playground.tensorflow.org\/#activation=tanh&amp;batchSize=10&amp;dataset=circle&amp;regDataset=reg-plane&amp;learningRate=0.03&amp;regularizationRate=0&amp;noise=0&amp;networkShape=4,2&amp;seed=0.66317&amp;showTestData=false&amp;discretize=false&amp;percTrainData=50&amp;x=true&amp;y=true&amp;xTimesY=false&amp;xSquared=false&amp;ySquared=false&amp;cosX=false&amp;sinX=false&amp;cosY=false&amp;sinY=false&amp;collectStats=false&amp;problem=classification&amp;initZero=false&amp;hideText=false\">link<\/a><\/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;\">\u00a018\/abr<\/td>\n<td style=\"border: 1px solid #cccccc;\">Redes neurais &#8211; convnets (<a href=\"https:\/\/www.dropbox.com\/s\/ptvpyd3szofbrv2\/AM%2017%20%28convnets%29.pptx?dl=0\">AM17<\/a>)<br \/>\nRedes Neurais &#8211; Tutorial: PyTorch (<a href=\"https:\/\/docs.google.com\/presentation\/d\/1nKrR1ttedN-n36dCtI5OfSGmwlrw17D5ZJT_08eKWbE\/edit?usp=sharing\">AM18<\/a>)<br \/>\nTutorial Pytorch &#8211; <a href=\"https:\/\/github.com\/rafaela00castro\/pytorch-hands-on\/blob\/master\/mnist_cnn.ipynb\">jupyter notebook<\/a><\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><a href=\"https:\/\/cs.nyu.edu\/~fergus\/papers\/zeilerECCV2014.pdf\">Visualizing and Understanding CNNs<\/a><br \/>\n<a href=\"http:\/\/brohrer.github.io\/how_convolutional_neural_networks_work.html\">How do CNNs work<\/a><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/intuitively-understanding-convolutions-for-deep-learning-1f6f42faee1\">Intuitively Understanding Convolutions<\/a><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a09<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a025\/abr<\/td>\n<td style=\"border: 1px solid #cccccc;\">Redes neurais &#8211; autoencoders (<a href=\"https:\/\/www.dropbox.com\/s\/hpc9j40je7e3pkh\/AM%2019%20%28ANNs%20-%20autoencoders%29.pptx?dl=0\">AM_19<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><a href=\"https:\/\/distill.pub\/2017\/feature-visualization\/\">Feature Visualization<\/a><br \/>\n<a href=\"http:\/\/www.evolvingai.org\/fooling\">Deep neural networks are easily fooled<\/a><br \/>\n<a href=\"https:\/\/blog.ycombinator.com\/how-adversarial-attacks-work\/\">How Adversarial Attacks Work<\/a><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a010<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a002\/mai<\/td>\n<td style=\"border: 1px solid #cccccc;\">Redes neurais &#8211; RNNs (<a href=\"https:\/\/www.dropbox.com\/s\/ihi6j45cxxcex9z\/AM%2020%20%28ANNs%20-%20recurrent%20nets%29.pptx?dl=0\">AM_20<\/a>); Detec\u00e7\u00e3o de Anomalias (<a href=\"https:\/\/www.dropbox.com\/s\/6etokwqpgt2so4d\/AM%2021%20%28Detec%C3%A7%C3%A3o%20de%20Anomalias%29.pptx?dl=0\">AM_21<\/a>)<\/td>\n<td style=\"border: 1px solid #cccccc;\"><a href=\"http:\/\/cucis.ece.northwestern.edu\/projects\/DMS\/publications\/AnomalyDetection.pdf\">paper<\/a>; <a href=\"https:\/\/cefetrjbr-my.sharepoint.com\/:u:\/r\/personal\/02848884789_cefet-rj_br\/Documents\/am-aulas2018\/AM-gaussians.ipynb?csf=1&amp;e=JM7dXW\">gaussians.ipynb<\/a><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a011<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">\u00a009\/mai<\/td>\n<td style=\"border: 1px solid #cccccc;\">Agrupamento (<a href=\"https:\/\/www.dropbox.com\/s\/g5lsvrqv9jzzrfh\/AM%2022%20%28Agrupamento%29%20.pptx?dl=0\">AM_22<\/a>)<br \/>\nRedu\u00e7\u00e3o de Dimensionalidade (<a href=\"https:\/\/www.dropbox.com\/s\/n1lt9j5h62b5wp5\/AM%2023%20%28Redu%C3%A7%C3%A3o%20de%20Dimensionalidade%29.pptx?dl=0\">AM_23<\/a>)<br \/>\nFechamento\/retrospectiva (<a href=\"https:\/\/www.dropbox.com\/s\/0bewkzqg1kmrvoi\/AM%2024%20%28Fechamento%29.pptx?dl=0\">AM_24<\/a>)<br \/>\n<a href=\"https:\/\/www.dropbox.com\/sh\/2jj5v89l8ddkpws\/AAAjTSQBZ-CvuXY1uxBSE5g6a?dl=0\">Notebooks<\/a><\/td>\n<td style=\"border: 1px solid #cccccc;\"><a href=\"https:\/\/docs.google.com\/forms\/d\/e\/1FAIpQLSev8Srcg103RACJjqDRy7z9RVrJA7st_3-GgZuq4rK1g5p14A\/viewform\">Formul\u00e1rio de avalia\u00e7\u00e3o do curso<\/a><\/p>\n<p>Alpaydin, Caps. 6 e 7; <a href=\"https:\/\/cefetrjbr-my.sharepoint.com\/:u:\/r\/personal\/02848884789_cefet-rj_br\/Documents\/am-aulas2018\/AM-PCA.ipynb?csf=1&amp;e=pMaDSM\">PCA.ipynb<\/a>; <a href=\"https:\/\/www.youtube.com\/watch?v=EokL7E6o1AE\">SVD<\/a>; <a href=\"https:\/\/www.youtube.com\/watch?v=kjBOesZCoqc&amp;list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab\">linear algebra<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr size=\"4\" width=\"100%\" \/>\n<h3>Trabalhos pr\u00e1ticos<\/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;\">Entrega: 07\/abril<\/span>.<\/li>\n<li><a href=\"https:\/\/www.dropbox.com\/s\/9im2suagvyj5dwv\/am-2019-trabalho02.pdf?dl=0\">T2<\/a>\u00a0&#8211;\u00a0<span style=\"color: #ff0000;\">Entrega: 21\/abril<\/span>.<\/li>\n<li><a href=\"https:\/\/www.dropbox.com\/s\/l1ciyisotw4kevw\/am-2019-trabalho03.pdf?dl=0\">T3<\/a>\u00a0&#8211; <span style=\"color: #ff0000;\">Entrega: <del>12\/maio<\/del> 19\/maio<\/span>.<\/li>\n<\/ul>\n<hr size=\"4\" width=\"100%\" \/>\n<h3>Recursos externos<\/h3>\n<ul>\n<li>V\u00eddeo: <a href=\"https:\/\/www.youtube.com\/watch?v=iX5V1WpxxkY\">Recurrent Neural Networks, Image Captioning, LSTM<\/a>, Andrej Karpathy.<\/li>\n<li>Curso online: <a href=\"http:\/\/course.fast.ai\">Practical Deep Learning For Coders<\/a><\/li>\n<li>Curso online: <a href=\"https:\/\/www.coursera.org\/learn\/neural-networks-deep-learning\">Neural Networks and Deep Learning<\/a><\/li>\n<li>Curso online: <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>Livros<\/h3>\n<ul>\n<li>David Poole &amp; . Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge, MA, 1 edition edition, August 2012.<\/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, 1 edition edition, November 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<li>Voc\u00ea pode pesquisar livros relevantes no acervo da <a href=\"http:\/\/biblioteca.cefet-rj.br\/\">Biblioteca do CEFET\/RJ<\/a>.<\/li>\n<\/ul>\n<p>Livros de interesse geral sobre Aprendizado de M\u00e1quina<\/p>\n<ul>\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<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>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<\/ul>\n<hr \/>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; &nbsp; &nbsp; Cursos Mestrado em Ci\u00eancia da Computa\u00e7\u00e3o Local\/hor\u00e1rio Bloco E, 5o andar, sala E-515 Dia\/hor\u00e1rio: 5as-feiras, das 13:25h \u00e0s 17:00h Apresenta\u00e7\u00e3o 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, [&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-1156","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/1156","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=1156"}],"version-history":[{"count":28,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/1156\/revisions"}],"predecessor-version":[{"id":1287,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/1156\/revisions\/1287"}],"wp:attachment":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/media?parent=1156"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}