{"id":2264,"date":"2023-09-22T13:13:04","date_gmt":"2023-09-22T13:13:04","guid":{"rendered":"https:\/\/eic.cefet-rj.br\/~ebezerra\/?page_id=2264"},"modified":"2026-04-27T10:38:49","modified_gmt":"2026-04-27T10:38:49","slug":"cic1205","status":"publish","type":"page","link":"https:\/\/eic.cefet-rj.br\/~ebezerra\/cic1205\/","title":{"rendered":"Aprendizado de M\u00e1quina"},"content":{"rendered":"<p><a href=\"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2025\/03\/dilbert-ml.gif\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-2669 aligncenter\" src=\"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2025\/03\/dilbert-ml-300x94.gif\" alt=\"\" width=\"680\" height=\"213\" srcset=\"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2025\/03\/dilbert-ml-300x94.gif 300w, https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-content\/uploads\/2025\/03\/dilbert-ml-768x240.gif 768w\" sizes=\"auto, (max-width: 680px) 100vw, 680px\" \/><\/a><\/p>\n<hr \/>\n<h3>Turmas<\/h3>\n<ul>\n<li>CIC1205 &#8211; Aprendizado de M\u00e1quina (P\u00f3s-gradua\u00e7\u00e3o)<\/li>\n<li>GCC1932 &#8211; Aprendizado de M\u00e1quina (Gradua\u00e7\u00e3o)<\/li>\n<\/ul>\n<h3>Local\/hor\u00e1rio<\/h3>\n<ul>\n<li>CEFET\/RJ, Maracan\u00e3, Bloco E, 5\u2060\u00ba andar, sala 513<\/li>\n<li>5\u2060\u1d43\u02e2-feiras, das 07h55 \u00e0s 11h30<\/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<table class=\"center\" style=\"width: 100%; table-layout: fixed;\"><colgroup> <col style=\"width: 15%;\" \/> <col style=\"width: 85%;\" \/> <\/colgroup>\n<tbody>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\"><b>Date<\/b><\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><b>Lecture<\/b><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">26\/02<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><a href=\"https:\/\/www.dropbox.com\/scl\/fi\/jlpuu7foghzpf6dklj11m\/ML00-Apresenta-o-do-curso.pptx?rlkey=j2hqjyoxobnth8rof5dodub69&amp;dl=0\">Course logistics<\/a><br \/><a href=\"https:\/\/www.dropbox.com\/scl\/fi\/3anw270shqnf4evw15hzp\/ML01-ML-overview.pptx?rlkey=2auy9qh5mntmp1bp00yt02bvg&amp;dl=0\">ML overview<\/a><br \/>Linear regression (<a href=\"https:\/\/www.dropbox.com\/scl\/fi\/kl51nsddew4zkj83g9c76\/ML02-Linear-Regression.pptx?rlkey=6lf3nkssr1rif8e0zqakaej1p&amp;dl=0\">slides<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/01_linear_regression.pdf\">lecture notes<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/tree\/main\/notebooks\/linear_regression\">code<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">05\/03<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Logistic regression (<a href=\"https:\/\/www.dropbox.com\/scl\/fi\/qvi2vi2nw8z8jm9d52k20\/ML04-Logistic-Regression.pptx?rlkey=ud4uznlzs79jkq4u2q57l12hr&amp;dl=0\">slides<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/02_logistic_regression.pdf\">lecture notes<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/tree\/main\/notebooks\/logistic_regression\">code<\/a>)<br \/>Decision trees (<a href=\"https:\/\/www.dropbox.com\/scl\/fi\/lo50z45n5iiuiroqlm6k0\/ML06-Decision-tree-learning.pptx?rlkey=yxtyvd3mo79gptwp21n2zn6g3&amp;dl=0\">slides<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/03_decision_trees.pdf\">lecture notes<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/tree\/main\/notebooks\/decision_trees\">code<\/a>)<br \/>kNN\u00a0(<a href=\"https:\/\/www.dropbox.com\/scl\/fi\/fh8ehty9alfp6n2v5ci5a\/ML07-kNN.pptx?rlkey=8dy02jvdgxojohfb8q9fryxr8&amp;dl=0\">slides<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/04_knn.pdf\">lecture notes<\/a>,\u00a0<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/tree\/main\/notebooks\/knn\">code<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">12\/03<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Model evaluation (<a href=\"https:\/\/www.dropbox.com\/scl\/fi\/53kpmeyr8otzfnx6272lx\/ML08-Model-Evaluation.pptx?rlkey=lqbbsehsg55cy4fubw9k954cf&amp;dl=0\">slides<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/05_model_evaluation.pdf\">lecture notes<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/tree\/main\/notebooks\/model_evaluation\">code<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">19\/03<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Model selection (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/06_model_selection.pdf\">lecture notes<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/tree\/main\/notebooks\/model_selection\">code<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">26\/03<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Hyperparameter search strategies (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/07_hyperparameter_search_strategies.pdf\">lecture notes<\/a>)<br \/>Class imbalance (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/08_class_imbalance.pdf\">lecture_notes<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/tree\/main\/notebooks\/class_imbalance\">code<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">02\/04<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Feature encoding (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/10_feature_encoding.pdf\">lecture_notes<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/tree\/main\/notebooks\/feature_preprocessing\">code<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">09\/04<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><strong>1st exam (first 30 minutes of this class)<br \/><\/strong>Model calibration (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/09_model_calibration.pdf\">lecture_notes<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/tree\/main\/notebooks\/model_calibration\">code<\/a>)<br \/>Feature scaling (lecture_notes, code)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">16\/04<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Polynomial features (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/13_polynomial_features.pdf\">lecture notes<\/a>, code)<br \/>Ensemble learning (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/12_ensemble_learning.pdf\">lecture_notes<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/tree\/main\/notebooks\/ensemble_learning\">code<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">23\/04<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">&#8211;Feriado&#8211;<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">30\/04<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Conformal prediction (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/14_conformal_prediction.pdf\">lecture_notes<\/a>, code)<br \/>Dimensionality reduction (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/lecture_notes\/15_dimensionality_reduction.pdf\">lecture_notes<\/a>, code)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">07\/05<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Multi-layer preceptrons<br \/>Model diagnostics (<a href=\"https:\/\/github.com\/MLRG-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/model_diagnostics.ipynb\">notebook<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">14\/05<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><a href=\"https:\/\/www.dropbox.com\/scl\/fi\/h0i4swgz2ojazt37rtws2\/ML10-Regularization.pptx?rlkey=wbi6lyaqvgqlo1c3m6e9tgcg8&amp;dl=0\">Model regularization<\/a> (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/model_regularization_L1_L2.ipynb\">notebook<\/a>)<br \/>Multi-layer Perceptron (<a href=\"https:\/\/github.com\/AIRGOLAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/DL03-mlp.ipynb\">notebook<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">21\/05<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><strong>2nd exam (first 30 minutes of this class)<\/strong><br \/><a href=\"https:\/\/www.dropbox.com\/scl\/fi\/lfl8dho3psn0w26zntruy\/ML15-SHAP-Values-Intro-.pptx.pdf?rlkey=1etct73lqq16dcjv4ds3a6yod&amp;dl=0\">SHAP Values<\/a> (notebooks: <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/shap_values_intro.ipynb\">1<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/shap_values_tutorial-0.ipynb\">2<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/shap_values_tutorial-1.ipynb\">3<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/shap_values_tutorial-3.ipynb\">4<\/a>)<br \/><a href=\"https:\/\/www.dropbox.com\/scl\/fi\/3uowytg0rsvkbit6qzop8\/ML16-Closing-Thoughts.pptx?rlkey=l73hcfpz02h8399uypdo2nf38&amp;dl=0\">Closing Thoughs<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<table class=\"center\" style=\"width: 100%; table-layout: fixed;\"><colgroup> <col style=\"width: 15%;\" \/> <col style=\"width: 85%;\" \/> <\/colgroup>\n<tbody>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\"><b>Date<\/b><\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><b>Lecture<\/b><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">28\/05<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">PyTorch basics (<a href=\"https:\/\/github.com\/AIRGOLAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/DL01.ipynb\">notebook<\/a>)<br \/>Perceptron; backpropagation (<a href=\"https:\/\/github.com\/AIRGOLAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/DL02-linear_model.ipynb\">notebook<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">04\/06<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Autoencoders (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/DL08-autoencoders.ipynb\">notebook<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/DL08-vae_mnist_latent_vis.ipynb\">notebook<\/a>)<br \/>Convolutional Neural Nets &#8211; 2D (<a href=\"https:\/\/github.com\/AIRGOLAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/DL04-cnn2d.ipynb\">notebook<\/a>)<br \/>Convolutional Neural Nets &#8211; 1D (<a href=\"https:\/\/github.com\/AIRGOLAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/DL05-cnn1d.ipynb\">notebook<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">11\/06<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">LSTM nets (<a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/DL07-LSTM_toy_example.ipynb\">notebook<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/DL07-LSTM.ipynb\">notebook<\/a>)<br \/>Dropout &amp; Early Stopping (<a href=\"https:\/\/github.com\/AIRGOLAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/DL06.ipynb\">notebook<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">18\/06<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Word2Vec (<a href=\"https:\/\/www.dropbox.com\/scl\/fi\/67i24b42lrbrbz1y0m5mq\/NLP-LanguageModels.pptx?rlkey=cq8lf0ymzkiip4m6atx674pfk&amp;dl=0\">slides<\/a>, <a href=\"https:\/\/github.com\/AILAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/misc\/skip-gram.ipynb\">notebook<\/a>)<br \/>Transformer Architecture (<a href=\"https:\/\/www.dropbox.com\/scl\/fi\/qu6a7r2w5x3bz3lyk7gz1\/NLP-Transformers.pptx?rlkey=g4fk2kb5l64u2wgo25ipxz4bg&amp;dl=0\">slides<\/a>, <a href=\"https:\/\/github.com\/AIRGOLAB-CEFET-RJ\/cic1205\/blob\/main\/notebooks\/DL\/misc\/types_of_transformers.ipynb\">notebook<\/a>)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">25\/06<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\">Project Presentations<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #cccccc; text-align: center;\">02\/07<\/td>\n<td style=\"border: 1px solid #cccccc; text-align: left;\"><strong>Final exam<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\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\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Turmas CIC1205 &#8211; Aprendizado de M\u00e1quina (P\u00f3s-gradua\u00e7\u00e3o) GCC1932 &#8211; Aprendizado de M\u00e1quina (Gradua\u00e7\u00e3o) Local\/hor\u00e1rio CEFET\/RJ, Maracan\u00e3, Bloco E, 5\u2060\u00ba andar, sala 513 5\u2060\u1d43\u02e2-feiras, das 07h55 \u00e0s 11h30 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 [&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-2264","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/2264","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=2264"}],"version-history":[{"count":101,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/2264\/revisions"}],"predecessor-version":[{"id":2950,"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/pages\/2264\/revisions\/2950"}],"wp:attachment":[{"href":"https:\/\/eic.cefet-rj.br\/~ebezerra\/wp-json\/wp\/v2\/media?parent=2264"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}