{"id":1504,"date":"2020-10-16T17:01:24","date_gmt":"2020-10-16T17:01:24","guid":{"rendered":"https:\/\/eic.cefet-rj.br\/~ebezerra\/?page_id=1504"},"modified":"2021-10-05T10:37:59","modified_gmt":"2021-10-05T10:37:59","slug":"aprendizado-de-maquina-2020-1","status":"publish","type":"page","link":"https:\/\/eic.cefet-rj.br\/~ebezerra\/aprendizado-de-maquina-2020-1\/","title":{"rendered":"GCC1932 &#8211; Aprendizado de M\u00e1quina (2020.1)"},"content":{"rendered":"<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<hr \/>\n<h3>Local\/hor\u00e1rio\/turma<\/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<li>Turma 901932<\/li>\n<\/ul>\n<hr \/>\n<h3>Vis\u00e3o geral<\/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\/wvifjwbeo1jdy5a\/AM-PlanoCurso%20%282020.1%29.xlsx?dl=0\"><strong>plano do curso<\/strong><\/a>. Veja tamb\u00e9m o <a href=\"http:\/\/www.cefet-rj.br\/attachments\/article\/199\/MARACAN\u00c3%20-%20CALENDARIO%20GRADUACAO%202020%20REV10.pdf\"><strong>calend\u00e1rio acad\u00eamico das gradua\u00e7\u00f5es<\/strong><\/a> do CEFET\/RJ.<\/p>\n<p>1017\/dezRedes neurais profundas para Vis\u00e3o Computacional<\/p>\n<p>1007\/janAprendizado por refor\u00e7o profundo; Tutorial OpenAI Gym<\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Class<\/b><\/td>\n<td><b>Date<\/b><\/td>\n<td><b>Lectures<\/b><\/td>\n<td><b>Readings<\/b><\/td>\n<\/tr>\n<tr>\n<td>1<\/td>\n<td>22\/out<\/td>\n<td>Vis\u00e3o geral do curso (<a href=\"https:\/\/www.dropbox.com\/s\/foaponupmrlq4uj\/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\/nr95zgs6sazn3sm\/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\/c56g2qbta1tk0fz\/AM%2002%20%28Regress%C3%A3o%20Linear%20com%20Uma%20Vari%C3%A1vel%29.pptx?dl=0\">AM02<\/a>)<\/td>\n<td>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>\u00a02<\/td>\n<td>29\/out<\/td>\n<td>Regress\u00e3o linear com v\u00e1rias vari\u00e1veis (<a href=\"https:\/\/www.dropbox.com\/s\/866hr6ougqhz2du\/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><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>\u00a03<\/td>\n<td>05\/nov<\/td>\n<td>Regulariza\u00e7\u00e3o (<a href=\"https:\/\/www.dropbox.com\/s\/3f630rtlls9rzip\/AM%2005%20%28Regulariza%C3%A7%C3%A3o%29.pptx?dl=0\">AM05<\/a>)<br \/>\nkNN (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_knn.ipynb\">AM06<\/a>) &#8211; conte\u00fado ass\u00edncrono<br \/>\nDecision Trees (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_dtree.ipynb\">AM07<\/a>) &#8211; conte\u00fado ass\u00edncrono<\/td>\n<td>(<a href=\"https:\/\/www.dropbox.com\/s\/cpmncilgkmsv6j0\/1811.12808.pdf?dl=0\">r3<\/a>)<\/td>\n<\/tr>\n<tr>\n<td>\u00a04<\/td>\n<td>\u00a012\/nov<\/td>\n<td>Model Evaluation (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_modeleval_sup.ipynb\">AM08<\/a>)<\/td>\n<td><a href=\"http:\/\/robotics.stanford.edu\/~ronnyk\/vote.pdf\">r4<\/a>)<\/td>\n<\/tr>\n<tr>\n<td>5<\/td>\n<td>19\/nov<\/td>\n<td>Naive Bayes Classifier (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_naivebayes.ipynb\">AM09<\/a>) &#8211; conte\u00fado ass\u00edncrono<br \/>\nModel Selection (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_modelselection.ipynb\">AM10<\/a>)<\/td>\n<td>(<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>)<\/td>\n<\/tr>\n<tr>\n<td>6<\/td>\n<td>26\/nov<\/td>\n<td>Clustering (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ml_clustering.ipynb\">AM11<\/a>) &#8211; conte\u00fado ass\u00edncrono<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>7<\/td>\n<td>03\/dez<\/td>\n<td>Tutorial Keras (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ppcic_ml_ann_keras.ipynb\">AM12<\/a>)<\/td>\n<td>\u00a0<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>8<\/td>\n<td>10\/dez<\/td>\n<td>Dimensionality Reduction (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/ml-class\/blob\/master\/ml_ppcic_dim_reduc.ipynb\">AM13<\/a>) &#8211; conte\u00fado ass\u00edncrono<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>9<\/td>\n<td>17\/dez<\/td>\n<td>Redes neurais convolucionais (<a href=\"https:\/\/www.dropbox.com\/s\/enrm3bntf49a7n3\/AM%2014%20%28convnets%29.pptx?dl=0\">AM14<\/a>)<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>10<\/td>\n<td>07\/jan<\/td>\n<td>Aprendizado por refor\u00e7o profundo; Tutorial OpenAI Gym<\/td>\n<td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>Projects<\/h3>\n<ul>\n<li><a href=\"https:\/\/www.dropbox.com\/s\/eo0m8m4hbf64soo\/GCC1932-2020.1-T1.pdf?dl=0\">T1<\/a> &#8211; 23\/nov<\/li>\n<li><a href=\"https:\/\/www.dropbox.com\/s\/ucuamn1gxluhwsg\/GCC1932-2020.1-T2.pdf?dl=0\">T2\u00a0<\/a>&#8211;\u00a013\/dez<\/li>\n<li><a href=\"https:\/\/www.dropbox.com\/s\/48vpodb0exx058t\/GCC1932-2020.1-T3.pdf?dl=0\">T3\u00a0<\/a>&#8211; 10\/jan<\/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>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>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>Brett Lantz. Machine Learning with R . Packt Publishing, Birmingham, October 2013.<\/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>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>Ian Goodfellow et al, <a href=\"http:\/\/www.deeplearningbook.org\/\">Deep Learning<\/a>, MIT Press, 2016.<\/li>\n<li>Jake VanderPlas, <a href=\"https:\/\/jakevdp.github.io\/PythonDataScienceHandbook\/\">Python Data Science Handbook<\/a>, 2016.<\/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>Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge, MA, 1 edition edition, August 2012.<\/li>\n<li>Marc Peter et al, <a href=\"https:\/\/mml-book.com\/\">Mathematics for Machine Learning<\/a>.<\/li>\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>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>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>Simon O. Haykin. Neural Networks and Learning Machines . Prentice Hall, New York, 3 edition edition, November 2008.<\/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>Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning From Data . AMLBook, S.l., March 2012.<\/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>Local\/hor\u00e1rio\/turma CEFET\/RJ, por videoconfer\u00eancia (plataforma MS Teams) Dia\/hor\u00e1rio: 5as-feiras, das 13:25h \u00e0s 17:00h Turma 901932 Vis\u00e3o geral 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-1504","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/1504","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=1504"}],"version-history":[{"count":24,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/1504\/revisions"}],"predecessor-version":[{"id":1851,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/1504\/revisions\/1851"}],"wp:attachment":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/media?parent=1504"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}