{"id":357,"date":"2016-07-12T10:55:41","date_gmt":"2016-07-12T13:55:41","guid":{"rendered":"http:\/\/eic.cefet-rj.br\/ppgcc\/?page_id=357"},"modified":"2025-09-16T21:34:43","modified_gmt":"2025-09-17T00:34:43","slug":"disciplinas","status":"publish","type":"page","link":"https:\/\/eic.cefet-rj.br\/ppcic\/disciplinas\/","title":{"rendered":"Disciplinas"},"content":{"rendered":"<p>O(A) aluno(a) do PPCIC dever\u00e1 integralizar as disciplinas obrigat\u00f3rias de An\u00e1lise e Projeto de Algoritmos e de Pesquisa para Disserta\u00e7\u00e3o de Mestrado, bem como atingir um total de cr\u00e9ditos igual ou superior a 24 (vinte e quatro). , assim distribu\u00eddos:<\/p>\n<ul>\n<li>3 (tr\u00eas) cr\u00e9ditos na disciplina obrigat\u00f3ria de An\u00e1lise e Projeto de Algoritmos<\/li>\n<li>m\u00ednimo de 6 (seis) cr\u00e9ditos em disciplinas do n\u00facleo b\u00e1sico;<\/li>\n<li>demais cr\u00e9ditos podem ser complementados com disciplinas do n\u00facleo espec\u00edfico.<\/li>\n<\/ul>\n<p>A inscri\u00e7\u00e3o em quaisquer disciplinas demanda a concord\u00e2ncia do Professor Orientador.<\/p>\n<p>A tabela seguir apresenta as principais disciplinas diretamente ligadas ao PPCIC. As ementas das disciplinas s\u00e3o apresentadas ap\u00f3s a tabela. Cabe ressaltar que as disciplinas apresentam as refer\u00eancias b\u00e1sicas que s\u00e3o complementados por artigos cient\u00edficos mais atualizados que os livros base.<\/p>\n<blockquote>\n<h3 style=\"text-align: left;\"><strong>Disciplinas\u00a0<\/strong><\/h3>\n<\/blockquote>\n<table width=\"90%\">\n<tbody>\n<tr>\n<td>Disciplina<\/td>\n<td>N\u00facleo<\/td>\n<td>Cr\u00e9ditos<\/td>\n<\/tr>\n<tr>\n<td>\u00c1lgebra Linear Computacional<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>\u00c1lgebra Linear e Grafos<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Algoritmos em Grafos<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>An\u00e1lise e Projeto de Algoritmos<\/td>\n<td>Obrigat\u00f3ria<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Aplica\u00e7\u00f5es de Rob\u00f3tica<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Aplica\u00e7\u00f5es Multim\u00eddia Interativas<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Aprendizado de M\u00e1quina<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Arquitetura de Computadores<\/td>\n<td>B\u00e1sico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Banco de Dados<\/td>\n<td>B\u00e1sico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Ci\u00eancia de Redes<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Computa\u00e7\u00e3o Paralela e Distribu\u00edda<\/td>\n<td>B\u00e1sico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Engenharia de Software<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Ger\u00eancia de Dados em Larga Escala<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Metodologia Cient\u00edfica em Computa\u00e7\u00e3o<\/td>\n<td>B\u00e1sico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>M\u00e9todos Estat\u00edsticos<\/td>\n<td>B\u00e1sico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Minera\u00e7\u00e3o de Dados<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Minera\u00e7\u00e3o de Processos<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Minera\u00e7\u00e3o de Textos<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Otimiza\u00e7\u00e3o por Metaheur\u00edsticas<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Programa\u00e7\u00e3o Linear<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>T\u00f3picos Especiais em Algoritmos<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>T\u00f3picos Especiais em Aplica\u00e7\u00f5es Computacionais<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>T\u00f3picos Especiais em Ger\u00eancia de Dados<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>T\u00f3picos Especiais em Intelig\u00eancia Computacional<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>T\u00f3picos Especiais em Modelagem<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>T\u00f3picos Especiais em Multim\u00eddia<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>T\u00f3picos Especiais em Otimiza\u00e7\u00e3o<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>T\u00f3picos Especiais em Programa\u00e7\u00e3o<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Visualiza\u00e7\u00e3o de Dados<\/td>\n<td>Espec\u00edfico<\/td>\n<td>3<\/td>\n<\/tr>\n<tr>\n<td>Estudo Orientado<\/td>\n<td>Espec\u00edfico<\/td>\n<td>2<\/td>\n<\/tr>\n<tr>\n<td>Pesquisa para a Disserta\u00e7\u00e3o de Mestrado<\/td>\n<td>&#8211;<\/td>\n<td>0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<blockquote>\n<p style=\"text-align: center;\">Ementa das Disciplinas<\/p>\n<\/blockquote>\n<p><strong>\u00c1lgebra Linear Computacional<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Aspectos fundamentais (produtos, normas, determinante, grafos, complexidade computacional). Modelagem por Sistemas Lineares. An\u00e1lise de erro e condicionamento. M\u00e9todos Diretos na resolu\u00e7\u00e3o de Sistemas Lineares (Elimina\u00e7\u00e3o Gaussiana, Decomposi\u00e7\u00e3o). M\u00e9todos Iterativos: cl\u00e1ssicos e n\u00e3o cl\u00e1ssicos (Krylov). Estabilidade e Esparsidade. Autovalores e Autovetores. An\u00e1lise de Componentes Principais (PCA). Decomposi\u00e7\u00e3o SVD. Decomposi\u00e7\u00e3o Tensorial. Multigrid. T\u00e9cnicas de Paralelismo<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">G. Golub &amp; C. vanLoan, Matrix Computations; Johns Hopkins University Press (1996).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">L. Eld\u00e9n. Matrix Methods in Data Mining and Pattern Recognition; SIAM (2019).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">D.S. Watkins. Fundamentals of Matrix Computation. Wiley-Interscience (2010).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">L. N. Trefethen and D. Bau. Numerical Linear Algebra; SIAM (1997)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">G. Strang. Linear Algebra and Learning from Data;<span style=\"font-weight: 400;\"> Wellesley-Cambridge Press (2019).<\/span><\/li>\n<\/ol>\n<p><strong>\u00c1lgebra Linear\u00a0e Grafos<\/strong><\/p>\n<p>Grafos. Subgrafos e Supergrafos. Fam\u00edlias de Grafos Especiais. Passeios e Caminhos em grafos. Grafos bipartidos e sua caracteriza\u00e7\u00e3o. Caminhos e\u00a0ciclos eulerianos. Caminhos e ciclos hamiltonianos; T\u00e9cnicas de Provas:\u00a0indu\u00e7\u00e3o e contradi\u00e7\u00e3o em problemas de grafos. Matrizes associadas a\u00a0grafos. Autovalores de matrizes associadas a grafos. Isomorfismo em\u00a0Grafos. \u00c1rvores. \u00c1rvores Geradoras. \u00c1rvores Geradoras M\u00ednimas.\u00a0Conectividade. Colora\u00e7\u00e3o.<\/p>\n<ol>\n<li>Russel Merris. Graph Theory. John Wiley &amp; Sons. 2001<\/li>\n<li>J.A. Bondy. U.S. R. Murty. Graph Theory. Springer. 2008<\/li>\n<li>Algebraic Graph Theory, Chris Godsil e Gordon Royle, Springer, 2004.<\/li>\n<\/ol>\n<p><strong>Algoritmos em Grafos<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Introdu\u00e7\u00e3o \u00e0 Teoria dos Grafos. <\/span><span style=\"font-weight: 400;\">Representa\u00e7\u00e3o computacional: matriz e lista de adjac\u00eancia, lista de incid\u00eancia. <\/span><span style=\"font-weight: 400;\">Percursos em Grafos. Algoritmos\u00a0 (BFS e DFS) e aplica\u00e7\u00f5es de Percursos em Grafos. Ordena\u00e7\u00e3o Topol\u00f3gica. <\/span><span style=\"font-weight: 400;\">\u00c1<\/span><span style=\"font-weight: 400;\">rvores: \u00e1rvore geradora, problema da \u00e1rvore geradora de custo m\u00ednimo, algoritmos de Prim, Kruskal, aplica\u00e7\u00f5es. <\/span><span style=\"font-weight: 400;\">Caminhos M\u00ednimos: \u00fanica origem (Bellman-Ford, Dijkstra), caminho m\u00ednimo v\u00e1rias origens (Floyd-Warshall).\u00a0<\/span><span style=\"font-weight: 400;\">Algoritmos Gulosos. Programa\u00e7\u00e3o Din\u00e2mica. Fluxo M\u00e1ximo e Emparelhamento M\u00e1ximo.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms. The MIT Press, Cambridge, Mass, 3rd edition, July 2009.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Robert Sedgewick and Kevin Wayne. Algorithms. Addison-Wesley Professional, Upper Saddle River, NJ, 4th edition, March 2011.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vazirani. Algorithms. McGraw-Hill Education, Boston, 1 edition, September 2006.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SZWARCFITER, J.L.; MARKENZON, L. Estrutura de dados e seus algoritmos. 3a edi\u00e7\u00e3o. Rio de Janeiro: LTC Ed., 2010.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">BOAVENTURA NETTO, P.O. Grafos: teoria, modelos, algoritmos. 5a edi\u00e7\u00e3o rev. ampl. S\u00e3o Paulo: E. Blucher, 2012.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">GOLDBARG, M.C.; GOLDBARG, E. Grafos: conceitos, algoritmos e aplica\u00e7\u00f5es. Rio de Janeiro: Elsevier, 2012.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Algoritmos, S. Dasgupta, C. Papadimitriou, U. Varizani, McGraw-Hill, 2009.<\/span><\/li>\n<\/ol>\n<p><strong>An\u00e1lise e Projeto de Algoritmos<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Estruturas de dados, especifica\u00e7\u00e3o de algoritmos, an\u00e1lise de complexidade computacional, bem como classes de problemas P e NP, Algoritmos Gulosos, Divis\u00e3o e Conquista, Programa\u00e7\u00e3o Din\u00e2mica. S\u00e3o apresentados os m\u00e9todos gerais de organiza\u00e7\u00e3o de dados: hashing, \u00e1rvores, filas, listas, filas de prioridade.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms. The MIT Press, Cambridge, Mass, 3rd edition, July 2009.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Jon Kleinberg and Eva Tardos. Algorithm Design. Pearson, Boston, 1 edition, March 2005.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Robert Sedgewick and Kevin Wayne. Algorithms. Addison-Wesley Professional, Upper Saddle River, NJ, 4th edition, March 2011.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Donald E. Knuth. The Art of Computer Programming. Addison-Wesley Professional, Amsterdam, March 2011.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Andrii Gakhov. Probabilistic Data Structures and Algorithms for Big Data Applications. Deutsche Nationalbibliothek, February 2019<\/span><\/li>\n<\/ol>\n<p><strong>Aplica\u00e7\u00f5es de Rob\u00f3tica<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Fundamentos e Caracter\u00edsticas Gerais da Rob\u00f3tica: rob\u00f4s industriais e m\u00f3veis; sensores, atuadores, manipuladores, motores e microcontroladores. Microcontroladores: tipos, caracter\u00edsticas gerais, arquitetura e programa\u00e7\u00e3o. Modelamento de Rob\u00f4s M\u00f3veis: Rob\u00f4s para obten\u00e7\u00e3o de dados trat\u00e1veis. Estrat\u00e9gias para confec\u00e7\u00e3o de rob\u00f4s m\u00f3veis tipos VANTS (a\u00e9reos, mar\u00edtimos ou terrestres): Rob\u00f4s para obter dados relativos \u00e0 agricultura, sa\u00fade, reconhecimento de padr\u00f5es, entre outros. Rob\u00f3tica Educacional: Projetos de rob\u00f4s educativos; an\u00e1lise de desempenho e qualitativa dos projetos de rob\u00f4s educativos.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">\u00a0NIKU, S. B. (2020) Introduction to robotics analysis, systems, applications. 349 p. -10:1119527627, 3\u00aa ed, Willey Pub.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Almeida, R. (2016) Programa\u00e7\u00e3o de Sistemas Embarcados: Linguagem C para microcontroladores, GEN-LTC,\u00a0 .<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">CRISP, J. (2004) Introduction to Microprocessors and Microcontrollers. 4a ed., Elsevier, 2010.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SILVA R. B. &amp; BLISKTEIN, P. (2019). Rob\u00f3tica Educacional: Comp\u00eandio de experi\u00eancias inovadoras. Ed. Penso, SP.<\/span><\/li>\n<\/ol>\n<p><strong>Aplica\u00e7\u00f5es Multim\u00eddia Interativas<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Introdu\u00e7\u00e3o a sistemas multim\u00eddia. Apresenta\u00e7\u00e3o do conceito de m\u00eddia, junto com sua representa\u00e7\u00e3o para armazenamento e exibi\u00e7\u00e3o. <\/span><span style=\"font-weight: 400;\">\u00a0Modelos e linguagens para autoria multim\u00eddia. <\/span><span style=\"font-weight: 400;\">T\u00f3picos recentes em multim\u00eddia.\u00a0<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Multimedia Communications: Applications, Networks, Protocols, and Standards. F. Halsall, Addison-Wesley, 2000.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">MediaSync: Handbook on Multimedia Synchronization. Mario Montagud, Pablo Cesar, Fernando Boronat, Jack Jansen, Springer, 2018.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Handbook of Data Compression. David Salomon, Giovanni Motta, Springer, 2010.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">MPEG-V: Bridging the Virtual and Real World. Yoon, Kyoungro, et al. Academic Press, 2015.<\/span><\/li>\n<li aria-level=\"1\">LI, Ze-Nian; DREW, Mark S.; LIU, Jiangchuan.\u00a0Fundamentals of multimedia. Upper Saddle River (NJ):: Pearson Prentice Hall, 2004.<\/li>\n<\/ol>\n<p><strong>Aprendizado de M\u00e1quina<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Vis\u00e3o geral do Aprendizado de M\u00e1quina; Regress\u00e3o linear; Regress\u00e3o log\u00edstica; kNN; \u00c1rvores de decis\u00e3o; Avalia\u00e7\u00e3o de modelos; Sele\u00e7\u00e3o de modelos; Classificador Naive Bayes; Aprendizado de comit\u00eas (Bagging, Boosting, Stacked Generalization); Redes Neurais Artificiais (fundamentos, representa\u00e7\u00e3o, algoritmo backpropagation, regulariza\u00e7\u00e3o, arquiteturas convolucionais, aplica\u00e7\u00f5es); Aprendizado por refor\u00e7o; Agrupamento, Redu\u00e7\u00e3o de dimensionalidade.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Max Kuhn and Kjell Johnson, Feature Engineering and Selection: A Practical Approach for Predictive Models<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Jeremy Watt et al, Machine Learning Refined: Foundations, Algorithms, and Applications<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Aur\u00e9lien G\u00e9ron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, 2019.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Sebastian Raschka, Python Machine Learning, 3rd ed, Packt Publishing, 2019.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Jake VanderPlas, Python Data Science Handbook, 2016.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006. pdf github<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Ethen Alpaydin, Introduction to Machine Learning, MIT Press, 2010.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Ian Goodfellow et al, Deep Learning, MIT Press, 2016.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Tom Mitchell, Machine Learning, McGraw-Hill, 1997.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge, MA, 1 edition edition, August 2012.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Peter Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data . Cambridge University Press, Cambridge ; New York, 1st edition, 2012.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Christopher Bishop. Pattern Recognition and Machine Learning . Springer, New York, October 2007.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">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<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning From Data . AMLBook, S.l., March 2012.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Brett Lantz. Machine Learning with R . Packt Publishing, Birmingham, October 2013.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Simon O. Haykin. Neural Networks and Learning Machines . Prentice Hall, New York, 3 edition edition, November 2008.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Ani Adhikari &amp; John DeNero, Artificial Intelligence: Foundations of Computational Agents, second edition, Cambridge University Press, 2017.<\/span><\/li>\n<\/ol>\n<p><strong>Arquitetura de Computadores<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Introdu\u00e7\u00e3o \u00e0 organiza\u00e7\u00e3o de computadores. Sistemas de numera\u00e7\u00e3o. Hierarquias de mem\u00f3ria.\u00a0 Unidade Central de Processamento: componentes, ciclo de instru\u00e7\u00e3o. Dispositivos de entrada e sa\u00edda. Arquitetura de sistemas computacionais distribu\u00eddos: Tratamento de dados com Clusters, Arquitetura de computadores para Big Datas.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Tanenbaum A. S. &amp; Austin T. (2018). Structured Computer Organization. Willey, USA.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Stallings. W. (2018) Computer Organization and Architecture: International, Willey Ed., 14 edition, USA.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Hennessy, J. &amp;\u00a0 Patterson, D.. (2018) Computer Architecture: A Quantitative Approach. Morgan Kaufmann Publishers, Waltham, USA<span style=\"font-weight: 400;\">.<\/span><\/li>\n<\/ol>\n<p><strong>Banco de Dados<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Conceitos b\u00e1sicos de BD: Introdu\u00e7\u00e3o aos conceitos b\u00e1sicos de ger\u00eancia de bases de dados. Arquitetura de um SGBD. Evolu\u00e7\u00e3o dos modelos de dados. <\/span><span style=\"font-weight: 400;\">Modelos de dados de sistemas de bancos de dados: hier\u00e1rquico, rede, relacional, orientado a objetos, e relacional-objeto. <\/span><span style=\"font-weight: 400;\">O modelo relacional: <\/span><span style=\"font-weight: 400;\">anomalias de atualiza\u00e7\u00e3o, depend\u00eancias funcionais e formas normais. <\/span><span style=\"font-weight: 400;\">\u00c1lgebra relacional. <\/span><span style=\"font-weight: 400;\">Processamento<\/span><span style=\"font-weight: 400;\"> e otimiza\u00e7\u00e3o de consultas.<\/span><span style=\"font-weight: 400;\"> Projeto f\u00edsico de banco de dados.<\/span> <span style=\"font-weight: 400;\">Armazenamento e indexa\u00e7\u00e3o. <\/span><span style=\"font-weight: 400;\">Transa\u00e7\u00f5es e as propriedades ACID. Recupera\u00e7\u00e3o de falhas. Controle de Concorr\u00eancia. <\/span><span style=\"font-weight: 400;\">Seguran\u00e7a em sistemas de bancos de dados relacionais. Sistemas de bancos de dados NoSQL: teorema CAP. modelos orientados a documentos, grafos, chave-valor e colunar. Conceitos de<em> Big Data<\/em> e dados de <em>streaming<\/em>. An\u00e1lise de dados: data warehouses e sistemas OLAP. Conceitos b\u00e1sicos de sistemas de bancos de dados<\/span><span style=\"font-weight: 400;\"> distribu\u00eddos <\/span><span style=\"font-weight: 400;\">e paralelos<\/span><span style=\"font-weight: 400;\">. <\/span><span style=\"font-weight: 400;\">NewSQL e Polystores.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Ramez Elmasri, Shamkant B. Navathe. Fundamentals of Database Systems. <\/span><span style=\"font-weight: 400;\">7<\/span><span style=\"font-weight: 400;\">th ed. Pearson, 20<\/span><span style=\"font-weight: 400;\">15<\/span><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Abraham Silberschatz, Henry Korth, S. Sudarshan. Database System Concepts. <\/span><span style=\"font-weight: 400;\">7<\/span><span style=\"font-weight: 400;\">th ed. McGraw-Hill Science\/Engineering\/Math, 201<\/span><span style=\"font-weight: 400;\">9<\/span><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">M. Tamer O\u0308zsu, Patrick Valduriez. Principles of Distributed Database Systems. <\/span><span style=\"font-weight: 400;\">4th<\/span><span style=\"font-weight: 400;\"> ed. Springer, 201<\/span><span style=\"font-weight: 400;\">9<\/span><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Raghu Ramakrishnan and Johannes Gehrke. Database Management Systems. <\/span><span style=\"font-weight: 400;\">4th<\/span><span style=\"font-weight: 400;\"> ed. McGraw-Hill, 20<\/span><span style=\"font-weight: 400;\">14<\/span><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Carlos Coronel, Steven Morris. Database Systems: Design, Implementation, &amp; Management. 13th ed. Cengage, 2018.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Peter Lake, Paul Crowther. Concise Guide to Databases: A Practical Introduction. Springer-Verlag London, 2013.<\/span><\/li>\n<\/ol>\n<p><strong>Ci\u00eancia de Redes<\/strong><\/p>\n<p>Introdu\u00e7\u00e3o e motiva\u00e7\u00e3o. Representa\u00e7\u00e3o e Classifica\u00e7\u00e3o de redes complexas. Medidas para caracteriza\u00e7\u00e3o topol\u00f3gica de redes complexas: grau, coeficiente de aglomera\u00e7\u00e3o, n\u00famero de ciclos, comprimento dos menores caminhos, motivos, medidas de centralidade, medidas espectrais, medidas hier\u00e1rquicas, medidas fractais, estrutura de comunidades. Modelos e Algoritmos de Gera\u00e7\u00e3o de Redes Complexas: grafos aleat\u00f3rios, modelo <em>small world<\/em>, redes livre de escala, redes com estrutura hier\u00e1rquica, modelo de configura\u00e7\u00e3o e m\u00e9todos de amostragem. Medidas de robustez. Algoritmos: <em>page-rank<\/em>, grau de intermedia\u00e7\u00e3o, detec\u00e7\u00e3o de comunidades, sincroniza\u00e7\u00e3o, falhas em cascata, caminhadas aleat\u00f3rias. Processos din\u00e2micos em redes complexas: caminhadas aleat\u00f3rias, falhas e ataques, falhas em cascata, comunica\u00e7\u00e3o e congestionamento, propaga\u00e7\u00e3o de epidemias, propaga\u00e7\u00e3o de opini\u00f5es, sincroniza\u00e7\u00e3o e din\u00e2mica coletiva. Aplica\u00e7\u00f5es em Redes Complexas: Redes Sociais, <em>World Wide Web<\/em>, Bioinform\u00e1tica, Agricultura de precis\u00e3o, Malhas rodovi\u00e1rias, Processamento de Imagens, Reconhecimento de padr\u00f5es e <em>Machine Learning<\/em>.<\/p>\n<ol>\n<li>Albert-L\u00e1zsl\u00f3 Barab\u00e1si. Network Science. Cambridge University Press. 2016. Dispon\u00edvel em: http:\/\/networksciencebook.com\/<\/li>\n<li>Eric D. Kolaczyk. Statistical Analysis of Network Data: Methods And Models. Springer-Verlag New York. 2009.<\/li>\n<li>David Easley and Jon Kleinberg. Networks, Crowds, and Markets. Cambridge University Press. 2010. Dispon\u00edvel em: http:\/\/www.cs.cornell.edu\/home\/kleinber\/networks-book\/networks-book.pdf<\/li>\n<li>Filippo Menczer and Santo Fortunato and Clayton A. Davis. A First Course in Network Science. Cambridge University Press. 2020.<\/li>\n<\/ol>\n<p><strong>Computa\u00e7\u00e3o Paralela e Distribu\u00edda<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Arquiteturas, sistemas, algoritmos, modelos de programa\u00e7\u00e3o, linguagens e ferramentas de <\/span><i><span style=\"font-weight: 400;\">software<\/span><\/i><span style=\"font-weight: 400;\">. Os t\u00f3picos abordados incluem arquiteturas paralelas; sistemas de computa\u00e7\u00e3o paralelo e distribu\u00eddo, <\/span><span style=\"font-weight: 400;\">computa\u00e7\u00e3o em nuvem e em grades<\/span><span style=\"font-weight: 400;\">; algoritmos distribu\u00eddos e paralelos, estruturas de dados e metodologias de programa\u00e7\u00e3o; modelos de paraleliza\u00e7\u00e3o e distribui\u00e7\u00e3o (MPI, Map-Reduce, etc); an\u00e1lise de desempenho, e <\/span><span style=\"font-weight: 400;\">aplica\u00e7\u00f5es que envolvem an\u00e1lise de dados e <\/span><i><span style=\"font-weight: 400;\">workflows <\/span><\/i><span style=\"font-weight: 400;\">cient\u00edficos<\/span><span style=\"font-weight: 400;\">.<\/span><\/p>\n<ol>\n<li><span style=\"font-weight: 400;\">Peter Pacheco. An Introduction to Parallel Programming. Morgan Kaufmann Publishers Inc., 1 edition, 2011.<\/span><\/li>\n<li aria-level=\"1\"><span style=\"font-weight: 400;\">Georg Hager and Gerhard Wellein. Introduction to High-Performance Computing for Scientists and Engineers. CRC Press, Boca Raton, FL, 1 edition, July 2010.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Victor Eijkhout. Introduction to High-Performance Scientific Computing. LuLu.com, Raleigh, N.C., January 2015.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A. D. Kshemkalyani and M. Singhal. Distributed Computing Principles, Algorithms, and Systems. Cambridge University Press, 2008.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Nikos Antonopoulos and Lee Gillam. Cloud Computing: Principles, Systems and Applications. Springer, 2 edition, 2017.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills. Advanced Analytics with Spark: Patterns for Learning from Data at Scale. O\u2019Reilly Media, Beijing, 1 edition, April 2015.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">K. G. Srinivasa and Anil Kumar Muppalla. Guide to High-Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark. Springer, New York, NY, 1 edition, February 2015.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">B.S.P. Mishra, S. Dehuri, E. Kim and G.-N Wang. Techniques and Environments for Big Data Analysis &#8211; Parallel, Cloud, and Grid Computing, Springer, 1 edition, 2016. <\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Daniel C. M. de Oliveira, Ji Liu and Esther Pacitti. Data-Intensive Workflow Management: For Clouds and Data-Intensive and Scalable Computing Environments. Morgan-Claypool, 1 edition, 2019.<\/li>\n<\/ol>\n<p><strong>Engenharia de Software<\/strong><\/p>\n<p>Introdu\u00e7\u00e3o \u00e0 Engenharia de Software. Processo de desenvolvimento de software. Modelos de maturidade de processo de software. Qualidade de produto de software. Gerenciamento de configura\u00e7\u00e3o de software. Aplica\u00e7\u00e3o da engenharia de software a \u00e1reas espec\u00edficas de dom\u00ednio (Data Science, ML, Blockchain, etc).<\/p>\n<ol>\n<li>PRESSMAN, Roger S., Engenharia de Software \u2013 Uma Abordagem Profissional, 7\u00aa edi\u00e7\u00e3o, S\u00e3o Paulo: Mc Graw Hill, 2011.<\/li>\n<li>SOMMERVILLE, Ian, Engenharia de Software, 9\u00aa edi\u00e7\u00e3o, S\u00e3o Paulo: Pearson Education \u2013 Addison-Wesley, 2011.<\/li>\n<li>Artigos selecionados.<\/li>\n<\/ol>\n<p><strong>Ger\u00eancia de Dados em Larga Escala<\/strong><\/p>\n<p>Introdu\u00e7\u00e3o de conceitos fundamentais, tecnologias e aplica\u00e7\u00f5es inovadoras realizados ao processamento e an\u00e1lise de grandes volumes de dados (<em>BigData<\/em>). Explora as solu\u00e7\u00f5es tecnol\u00f3gicas mais recentes, dentre as quais as diferentes formas de organiza\u00e7\u00e3o de dados, incluindo abordagens sistemas de armazenamento distribu\u00eddos (HDFS), bancos de dados relacional-objeto, NoSQL e newSQL e suas liga\u00e7\u00f5es como t\u00e9cnica de paralelismo baseado no particionamento de dados.<\/p>\n<ol>\n<li>\u00a0M. Tamer Ozsu and Patrick Valduriez. Principles of Distributed Database\u00a0Systems. Springer, New York, 3rd ed. 2011 edition, March 2011.<\/li>\n<li>Peter Lake and Robert Drake. Information Systems Management in the Big\u00a0Data Era. Springer, New York, NY, 2014 edition, January 2015.<\/li>\n<li>Vijay Srinivas Agneeswaran. Big Data Analytics Beyond Hadoop: Real-Time\u00a0Applications with Storm, Spark, and More Hadoop Alternatives. Pearson FT\u00a0Press, Upper Saddle River, 1 edition, May 2014.<\/li>\n<li>Hrushikesha Mohanty, Prachet Bhuyan, and Deepak Chenthati, editors. Big\u00a0Data: A Primer. Springer, New York, NY, 2015 edition, July 2015.<\/li>\n<li>\u00a0Aboul-Ella Hassanien, Ahmad Taher Azar, Vaclav Snasel, Janusz Kacprzyk,\u00a0and Jemal H. Abawajy, editors. Big Data in Complex Systems: Challenges\u00a0and Opportunities. Springer, New York, 2015 edition, January 2015.<\/li>\n<li>Christine L. Borgman. Big Data, Little Data, No Data: Scholarship in the\u00a0Networked World. The MIT Press, Cambridge, Massachusetts, January 2015.<\/li>\n<li>Sandya Mannarswamy. Data Science: Learn the What, Where, and How of\u00a0Data Science. Apress, 2015 edition, June 2015.<\/li>\n<li>Tony Hey, Stewart Tansley, and Kristin Tolle, editors. The Fourth Paradigm:\u00a0Data-Intensive Scientific Discovery. Microsoft Research, Redmond,\u00a0Washington, 1 edition, October 2009.<\/li>\n<\/ol>\n<p><strong>Metodologia Cient\u00edfica em Computa\u00e7\u00e3o<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">A disciplina objetiva desenvolver habilidade para elabora\u00e7\u00e3o de artigos e projetos cient\u00edficos na \u00e1rea de computa\u00e7\u00e3o. Para isso, \u00e9 importante que o aluno tenha ci\u00eancia da import\u00e2ncia dos principais elementos vinculados \u00e0 pesquisa, desde a escolha do tema, defini\u00e7\u00e3o do problema, revis\u00e3o bibliogr\u00e1fica, execu\u00e7\u00e3o da pesquisa at\u00e9 o processo de escrita propriamente dito. Ementa: <\/span><span style=\"font-weight: 400;\">(i) introdu\u00e7\u00e3o e caracteriza\u00e7\u00e3o da pesquisa em Ci\u00eancia da Computa\u00e7\u00e3o<\/span><span style=\"font-weight: 400;\">; (ii)\u00a0 <\/span><span style=\"font-weight: 400;\">etapas<\/span><span style=\"font-weight: 400;\"> da pesquisa; (iii) revis\u00e3o bibliogr\u00e1fica; (iv) cita\u00e7\u00f5es <\/span><span style=\"font-weight: 400;\">de trabalhos e pl\u00e1gio<\/span><span style=\"font-weight: 400;\">; (v) elabora\u00e7\u00e3o de apresenta\u00e7\u00f5es; (vi) escrita cient\u00edfica; (vii) gr\u00e1ficos, <\/span><span style=\"font-weight: 400;\">diagramas <\/span><span style=\"font-weight: 400;\">e tabelas; (viii) formaliza\u00e7\u00e3o matem\u00e1tica e de algoritmos; (ix) avalia\u00e7\u00e3o experimental.\u00a0<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Justin Zobel. Writing for Computer Science. Springer, New York, NY, 3rd ed. 2014 edition, February 2015.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Raul Wazlawick. Metodologia de Pesquisa para Ci\u00eancia da Computa\u00e7\u00e3o. Elsevier, 2 edi\u00e7\u00e3o, September 2014.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hilary Glasman-Deal. Science Research Writing for Non-Native Speakers of English. Imperial College Press, London; Hackensack, NJ, 1 edition, December 2009.<\/span><\/li>\n<\/ol>\n<p><strong>M\u00e9todos Estat\u00edsticos<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Modelos probabil\u00edsticos e vari\u00e1veis aleat\u00f3rias unidimensionais e multidimensionais. Teorema de Bayes. Valores esperados e transforma\u00e7\u00f5es de vari\u00e1veis aleat\u00f3rias. Teorema Central do Limite. Introdu\u00e7\u00e3o aos m\u00e9todos de an\u00e1lise de dados univariados e \u00e0 infer\u00eancia estat\u00edstica. Estat\u00edsticas descritivas e m\u00e9todos de an\u00e1lise explorat\u00f3ria de dados. Vis\u00e3o geral de t\u00e9cnicas de amostragem para a recolha de dados e introdu\u00e7\u00e3o aos m\u00e9todos de infer\u00eancia estat\u00edstica para a tomada de decis\u00e3o, incluindo regress\u00e3o linear simples, procedimentos de estima\u00e7\u00e3o usando intervalos de confian\u00e7a e testes de hip\u00f3teses.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Peter Dalgaard. Introductory Statistics with R. Springer, New York, 2nd edition, August 2008.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Richard J. Larsen and Morris L. Marx. An Introduction to Mathematical Statistics and Its Applications. Prentice Hall, Upper Saddle River, N.J, 4 edition, December 2005.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Ronald E Walpole. Probability &amp; statistics for engineers &amp; scientists. Prentice Hall, Boston, 2012.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Jay L. Devore and Kenneth N. Berk. Modern Mathematical Statistics with Applications. Springer, New York; London, 2nd ed. 2012 edition, December 2011.<\/span><\/li>\n<\/ol>\n<p><strong>Minera\u00e7\u00e3o de Dados<\/strong><\/p>\n<p>A minera\u00e7\u00e3o consiste no processo de extra\u00e7\u00e3o de conhecimento a partir de dados. Os t\u00f3picos principais abordados neste curso incluem pr\u00e9-processamento, classifica\u00e7\u00e3o, agrupamento, regras de associa\u00e7\u00e3o, anomalia e o processo de minera\u00e7\u00e3o de dados propriamente dito. A disciplina objetiva fornecer aos alunos as compet\u00eancias fundamentais necess\u00e1rias para conduzir sua pr\u00f3pria investiga\u00e7\u00e3o em minera\u00e7\u00e3o de dados.<\/p>\n<ol>\n<li>\u00a0Mohammed J. Zaki and Wagner Meira Jr. Data Mining and Analysis: Fundamental\u00a0Concepts and Algorithms. Cambridge University Press, May 2014.<\/li>\n<li>Ian H. Witten, Eibe Frank, and Mark A. Hall. Data Mining: Practical Machine\u00a0Learning Tools and Techniques. Morgan Kaufmann, Burlington, MA,\u00a03 edition, January 2011.<\/li>\n<li>Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and\u00a0Techniques. Morgan Kaufmann, Waltham, Mass., 3 edition, July\u00a02011.<\/li>\n<li>\u00a0Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An\u00a0Introduction to Statistical Learning: with Applications in R. Springer, 1st\u00a0ed. 2013. corr. 4th printing 2014 edition, August 2013.<\/li>\n<li>Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of\u00a0Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2nd\u00a0ed. 2009. corr. 7th printing 2013 edition, April 2011.<\/li>\n<li>Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage\u00a0Data. Springer, softcover reprint of hardcover 2nd ed. 2011 edition,\u00a0August 2013.<\/li>\n<\/ol>\n<p><strong>Minera\u00e7\u00e3o de Processos<\/strong><\/p>\n<p>Conceitos sobre modelagem de processos de neg\u00f3cio (BPM). Modelos de processos e descoberta de processos de neg\u00f3cios. Diferentes tipos de modelos de processos. T\u00e9cnicas de descoberta de processos e an\u00e1lise de conformidade. Enriquecimento de modelos de processos. Suporte operacional.<\/p>\n<ol>\n<li>VAN DER AALST, Wil. <em>Process Mining: Data Science in Action<\/em>. 2<sup>nd<\/sup> Springer-Verlag, 2016.<\/li>\n<li>MANS, Ronny S., VAN DER AALST, Wil, VANWERSCH, Rob, J. B. <em>Process Mining in Healthcare: Evaluating and Exploiting Operational Healthcare Processes<\/em>. Springer Cham Heidelberg, 2015.<\/li>\n<li>Beheshti, Seyed-Mehdi-Reza, Benatallah, Boualem, Sakr, Sherif, Grigori, Daniela, Motahari-Nezhad, Hamid Reza, Barukh, Moshe, Chai, Gater, Ahmed, Ryu, Seung Hwan. <em>Process Analytics: Concepts and Techniques for Querying and Analyzing Process Data<\/em>. Springer International Publishing, 2016.<\/li>\n<li>Burattin, Andrea. <em>Process Mining Techniques in Business Environments: Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining (Lecture Notes in Business Information Processing)<\/em>. Series: Lecture Notes in Business Information Processing (Book 207). Springer; 2015.<\/li>\n<li>Provost, Foster, Fawcett, Tom. <em>Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking<\/em>. O&#8217;Reilly Media, 2013.<\/li>\n<li>Han, Jiawei, Kamber, Micheline, Pei, Jian. <em>Data Mining: Concepts and Techniques<\/em>. 3<sup>rd<\/sup> edition. The Morgan Kaufmann Series in Data Management Systems, 2011.<\/li>\n<\/ol>\n<p><strong>Minera\u00e7\u00e3o de\u00a0Textos<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Vis\u00e3o geral de minera\u00e7\u00e3o de textos e aplica\u00e7\u00f5es. Processamento de linguagem natural e Representa\u00e7\u00e3o de dados textuais. Processo de Descoberta de Conhecimento em Texto (KDT). An\u00e1lise Explorat\u00f3ria de Texto. Pr\u00e9-processamento de Texto: Tokenization; Stopwords; Stemming; Dicion\u00e1rio ou Thesaurus. Agrupamento e classifica\u00e7\u00e3o de textos. An\u00e1lise de sentimento e minera\u00e7\u00e3o de opini\u00f5es. M\u00e9tricas de Avalia\u00e7\u00e3o.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Charu C. Aggarwal. Machine Learning for Text. Springer International Publishing, 1st edition, April 2018.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Dipanjan Sarkar. Text Analytics with Python: A Practitioner&#8217;s Guide to Natural Language Processing. Apress, 2nd edition, May 2019.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Anne Kao and Steve R. Poteet. Natural Language Processing and Text Mining. Springer London, edi\u00e7\u00e3o: 1 edition, March 2007.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Christopher Manning and Hinrich Schuetze. Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge, Mass, 1 edition, June 1999.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Bing Liu. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, 1st edition, June 2015.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Charu Aggarwal and ChengXiang Zhai, editors. Mining Text Data. Springer, edi\u00e7\u00e3o: 2012 edition, February 2012.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Daniel Jurafsky, James Martin. Speech and Language Processing, 2nd edition, Prentice Hall. May 2008.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Kamath, Uday, John Liu, and James Whitaker. Deep learning for nlp and speech recognition. Vol. 84. Springer, 2019.<\/span><\/li>\n<\/ol>\n<p><strong>Otimiza\u00e7\u00e3o por Metaheur\u00edsticas<\/strong><\/p>\n<p>Introdu\u00e7\u00e3o \u00e0 Complexidade Computacional de Problemas e Algoritmos<span style=\"font-weight: 400;\">,<\/span> Fundamentos de modelagem <span style=\"font-weight: 400;\">(Caracteriza\u00e7\u00e3o de problemas de otimiza\u00e7\u00e3o;<\/span> <span style=\"font-weight: 400;\">Representa\u00e7\u00e3o de solu\u00e7\u00f5es;<\/span> <span style=\"font-weight: 400;\">Constru\u00e7\u00e3o de solu\u00e7\u00f5es: solu\u00e7\u00f5es parciais e incompletas.), <\/span>Heur\u00edsticas<span style=\"font-weight: 400;\"> (Heur\u00edsticas construtivas: Intui\u00e7\u00e3o e seu papel na constru\u00e7\u00e3o de uma solu\u00e7\u00e3o, algoritmos gulosos, algoritmos gulosos probabil\u00edsticos, m\u00e9todos do tipo <\/span><i><span style=\"font-weight: 400;\">multi-start<\/span><\/i><span style=\"font-weight: 400;\">, avalia\u00e7\u00e3o da qualidade;<\/span> <span style=\"font-weight: 400;\">M\u00e9todos de busca local: Vizinhan\u00e7a e topologia do espa\u00e7o de busca, an\u00e1lise de complexidade, heur\u00edsticas de melhoria (<\/span><i><span style=\"font-weight: 400;\">Hill Climbing<\/span><\/i><span style=\"font-weight: 400;\">);<\/span> <span style=\"font-weight: 400;\">M\u00e9todos de busca larga: Destruir-e-reconstruir, cadeias de eje\u00e7\u00e3o, <\/span><i><span style=\"font-weight: 400;\">set-covering<\/span><\/i><span style=\"font-weight: 400;\">.) e <\/span>Metaheur\u00edsticas <span style=\"font-weight: 400;\">(Conceito de Metaheur\u00edstica;<\/span> <span style=\"font-weight: 400;\">Classifica\u00e7\u00e3o de Metaheur\u00edsticas;Metaheur\u00edsticas Iterativas: <\/span><i><span style=\"font-weight: 400;\">Greedy Randomized Adaptive Search Procedure<\/span><\/i><span style=\"font-weight: 400;\"> (GRASP), <\/span><i><span style=\"font-weight: 400;\">Iterated Local Search<\/span><\/i><span style=\"font-weight: 400;\"> (ILS), Busca em Vizinhan\u00e7a Vari\u00e1vel (<\/span><i><span style=\"font-weight: 400;\">Variable Neighborhood Search<\/span><\/i><span style=\"font-weight: 400;\"> &#8211; VNS),<\/span> <span style=\"font-weight: 400;\">Recozimento Simulado (<\/span><i><span style=\"font-weight: 400;\">Simulated Annealing <\/span><\/i><span style=\"font-weight: 400;\">&#8211; SA), Busca Tabu<\/span> <span style=\"font-weight: 400;\">(<\/span><i><span style=\"font-weight: 400;\">Tabu Search <\/span><\/i><span style=\"font-weight: 400;\">&#8211; TS), Reconex\u00e3o por Caminhos (<\/span><i><span style=\"font-weight: 400;\">Path Relinking <\/span><\/i><span style=\"font-weight: 400;\">&#8211; PR);<\/span> <span style=\"font-weight: 400;\">Metaheur\u00edsticas Populacionais: Algoritmos Gen\u00e9ticos (<\/span><i><span style=\"font-weight: 400;\">Genetic Algorithms <\/span><\/i><span style=\"font-weight: 400;\">&#8211; GA), Col\u00f4nias de Formigas (<\/span><i><span style=\"font-weight: 400;\">Ant Colony Optimization <\/span><\/i><span style=\"font-weight: 400;\">&#8211; ACO), Enxame de Part\u00edculas (<\/span><i><span style=\"font-weight: 400;\">Particle Swarm Optimization <\/span><\/i><span style=\"font-weight: 400;\">&#8211; PSO);<\/span> <span style=\"font-weight: 400;\">Metodologias e processos de avalia\u00e7\u00e3o de Metaheur\u00edsticas;<\/span> <span style=\"font-weight: 400;\">Condu\u00e7\u00e3o de experimentos computacionais em Metaheur\u00edsticas.).<\/span><\/p>\n<ol>\n<li><span style=\"font-weight: 400;\">Gendreau, Michel; Jean-Yves, Potvin. <\/span><i><span style=\"font-weight: 400;\">Handbook of metaheuristics<\/span><\/i><span style=\"font-weight: 400;\">. Vol. 3. Springer, 2019.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Talbi, El-Ghazali. <\/span><i><span style=\"font-weight: 400;\">Metaheuristics: from design to implementation<\/span><\/i><span style=\"font-weight: 400;\">. <\/span><span style=\"font-weight: 400;\">Vol. 74. John Wiley &amp; Sons, 2009.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Talbi, El-Ghazali. <\/span><i><span style=\"font-weight: 400;\">Hybrid metaheuristics<\/span><\/i><span style=\"font-weight: 400;\">. Vol. 166. Berlin Heidelberg: Springer, 2013.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Cormen, Thomas H., et al. <\/span><i><span style=\"font-weight: 400;\">Introduction to algorithms<\/span><\/i><span style=\"font-weight: 400;\">. MIT press, 2009.<\/span><\/li>\n<\/ol>\n<p><strong>Programa\u00e7\u00e3o Linear<\/strong><\/p>\n<p>Introdu\u00e7\u00e3o \u00e0 Programa\u00e7\u00e3o Linear:<span style=\"font-weight: 400;\"> Conceito fundamentais; Variantes do problema de programa\u00e7\u00e3o linear: forma padr\u00e3o, forma can\u00f4nica, redu\u00e7\u00e3o para forma padr\u00e3o, exemplos de PPL; Modelagem: conceitos, etapas da formula\u00e7\u00e3o de um modelo matem\u00e1tico, exemplos; Representa\u00e7\u00e3o e solu\u00e7\u00e3o gr\u00e1fica; Geometria de PL: conceitos e defini\u00e7\u00f5es, poliedros, hiperplanos, semi-espa\u00e7os, conjuntos convexos, pontos extremos, solu\u00e7\u00e3o b\u00e1sica vi\u00e1vel, degeneresc\u00eancia, otimalidade de pontos extremos; Condi\u00e7\u00f5es de otimalidade. <\/span>M\u00e9todo Simplex: O M\u00e9todo Simplex e Simplex Revisado; M\u00e9todos de obten\u00e7\u00e3o de solu\u00e7\u00f5es b\u00e1sicas iniciais vi\u00e1veis (M\u00e9todo das Duas Fases, M-Grande); Degenera\u00e7\u00e3o e ciclagem; Regras de pivoteamento. Dualidade em Programa\u00e7\u00e3o Linear: Introdu\u00e7\u00e3o \u00e0 Teoria da Dualidade: problema dual, teorema da dualidade; Teorema da Exist\u00eancia; Teorema Fraco das Folgas Complementares; Teorema Forte das Folgas Complementares; Interpreta\u00e7\u00e3o Econ\u00f4mica da Dualidade; M\u00e9todo Simplex Dual. An\u00e1lise de Sensibilidade em Programa\u00e7\u00e3o Linear.<\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Bertsimas, D. and Tsitsiklis, J. N. Introduction to Linear Optimization. 1997.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Chvatal, Linear Programming, W.H. Freeman, New York, 1983<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Bazaraa, M. S. and Jarvis, J. J. and Sherali, H. D. Linear Programming and Network Flows. 2010.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hillier e Lieberman, Introdu\u00e7\u00e3o \u00e0 Pesquisa Operacional,<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Goldbarg, M. C. e Luna, H. P. Otimiza\u00e7\u00e3o Combinat\u00f3ria e Programa\u00e7\u00e3o Linear. 2.ed. 2005.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">McGraw-Hill, 2006 Arenales, Armentano, Morabito, Yanasse, Pesquisa Operacional, Editora Campus.<\/span><\/li>\n<\/ol>\n<p><strong>T\u00f3picos Especiais em Algoritmos<\/strong><\/p>\n<p>Desenvolvimento de algoritmos e an\u00e1lise de complexidade; m\u00e9todos de indu\u00e7\u00e3o matem\u00e1tica e projetos de algoritmos por indu\u00e7\u00e3o; projetos de algoritmos eficientes em problemas de natureza computacional; algoritmos em grafos; abordagem de estruturas de dados elementares e avan\u00e7adas; projeto e an\u00e1lise de algoritmos adaptativos.<\/p>\n<ol>\n<li>Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C., Algoritmos: teoria e pr\u00e1tica. Tradu\u00e7\u00e3o da 3\u00aa Edi\u00e7\u00e3o Americana, Elsevier Editora LTDA, 2012.<\/li>\n<\/ol>\n<p><strong>T\u00f3picos Especiais em Aplica\u00e7\u00f5es Computacionais\u00a0<\/strong><\/p>\n<div>\n<div>Desenvolvimento de prot\u00f3tipos ou artefatos computacionais ou modelos te\u00f3ricos. Aplica\u00e7\u00f5es em \u00e1reas fim, tais como engenharias, ci\u00eancias exatas, biol\u00f3gicas, humanas, economia ou ci\u00eancias ambientais.<\/div>\n<ol>\n<li>Raul Wazlawick. Metodologia de Pesquisa para Ci\u00eancia da Computa\u00e7\u00e3o. Elsevier, 2 edi\u00e7\u00e3o, September 2014.<\/li>\n<li>Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C., Algoritmos: teoria e pr\u00e1tica. Tradu\u00e7\u00e3o da 3\u00aa Edi\u00e7\u00e3o Americana, Elsevier Editora LTDA, 2012.<\/li>\n<\/ol>\n<\/div>\n<p><strong>T\u00f3picos Especiais em Ger\u00eancia de Dados<\/strong><\/p>\n<p>Desenvolvimento de prot\u00f3tipos, algoritmos ou artefatos computacionais envolvendo ger\u00eancia de dados de diferentes modelos e em diferentes escalas (incluindo Big Data) nos diferentes contextos de Ci\u00eancia de Dados e de arquiteturas (centralizada, paralela e distribu\u00edda). Esses prot\u00f3tipos far\u00e3o uso de um ou mais m\u00e9todos especializados de ger\u00eancia de dados em algum recorte de modelos de dados e dom\u00ednios de aplica\u00e7\u00f5es.<\/p>\n<ol>\n<li>M. Tamer Ozsu and Patrick Valduriez. Principles of Distributed Database Systems. Springer, New York, 3rd Ed. 2011 Edition, March 2011.<\/li>\n<li>Peter Lake and Robert Drake. Information Systems Management in The Big Data Era. Springer, New York, NY, 2014 Edition, January 2015.<\/li>\n<li>Vijay Srinivas Agneeswaran. Big Data Analytics Beyond Hadoop: Real-Time Applications With Storm, Spark, And More Hadoop Alternatives. Pearson Ft Press, Upper Saddle River, 1 Edition, May 2014.<\/li>\n<li>Aboul-Ella Hassanien, Ahmad Taher Azar, Vaclav Snasel, Janusz Kacprzyk, And Jemal H. Abawajy, Editors. Big Data in Complex Systems: Challenges And Opportunities. Springer, New York, 2015 Edition, January 2015.<\/li>\n<li>Tony Hey, Stewart Tansley, And Kristin Tolle, Editors. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, Redmond, Washington, 1 Edition, October 2009.<\/li>\n<\/ol>\n<p><strong>T\u00f3picos Especiais em Intelig\u00eancia Computacional<\/strong><\/p>\n<p>Desenvolvimento de prot\u00f3tipos, algoritmos ou artefatos computacionais envolvendo caracter\u00edsticas de aplica\u00e7\u00f5es de intelig\u00eancia computacional (tais como minera\u00e7\u00e3o de dados, minera\u00e7\u00e3o de texto, minera\u00e7\u00e3o de processo, aprendizagem de m\u00e1quina e aprendizagem estat\u00edstica) associados a modelos de dados presentes na Ci\u00eancia de Dados (tais como big data, s\u00e9ries temporais, s\u00e9ries espa\u00e7o-temporais, streaming, imagens, textos) em dom\u00ednios como, dentre outros, sa\u00fade, educa\u00e7\u00e3o, economia, transportes, rob\u00f3tica, redes sociais, cogni\u00e7\u00e3o e sentimentos. Esses prot\u00f3tipos far\u00e3o uso de um ou mais m\u00e9todos especializados de intelig\u00eancia computacional em algum recorte destes modelos\/dom\u00ednios.<\/p>\n<ol>\n<li>Han, Jiawei, Kamber, Micheline, Pei, Jian. Data Mining: Concepts and Techniques. 3rd edition. The Morgan Kaufmann Series in Data Management Systems, 2011.<\/li>\n<li>Rutkowski, Leszek (2008). Computational Intelligence: Methods and Techniques. Springer. ISBN\u00a0978-3-540-76288-1.<\/li>\n<li>VAN DER AALST, Wil. Process Mining: Data Science in Action. 2nd edition. Springer-Verlag, 2016<\/li>\n<li>KAO, Anne; POTEET, Stephen; Natural language processing and text mining. London: Springer 2007. ISBN 184628175.<\/li>\n<\/ol>\n<p><strong>T\u00f3picos Especiais em Modelagem<\/strong><\/p>\n<p>T\u00e9cnicas de Modelagem; T\u00e9cnicas de Simula\u00e7\u00e3o Computacional; An\u00e1lise de Complexidade; Aplica\u00e7\u00f5es em Problemas de Engenharia e Ci\u00eancias.<\/p>\n<ol>\n<li>Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C., Algoritmos: teoria e pr\u00e1tica. Tradu\u00e7\u00e3o da 3\u00aa Edi\u00e7\u00e3o Americana, Elsevier Editora LTDA, 2012.<\/li>\n<li>Shiflet, A.B., Shiflet, G.W. Introduction to Computational Science: Modeling and Simulation for the Sciences. Second Edition, Princeton University Press, 2014.<\/li>\n<\/ol>\n<p><strong>T\u00f3picos Especiais em Multim\u00eddia<\/strong><\/p>\n<p>Esta disciplina cobre os t\u00f3picos mais relevantes do momento na \u00e1rea de multim\u00eddia. Ela discute os conceitos, caracter\u00edsticas, padr\u00f5es e requisitos da modelagem de aplica\u00e7\u00f5es multim\u00eddias em diferentes contextos, abrangendo, mas n\u00e3o limitado a: Internet das Coisas, Efeitos Sensoriais e \u00e1reas afins.<\/p>\n<ol>\n<li>YOON, Kyoungro et al. \u201cMPEG-V: Bridging the Virtual and Real World\u201d. Academic Press, 2015.<\/li>\n<li>FURHT, Borko (Ed.). \u201cMultimedia Systems and Techniques\u201d. Springer Science &amp; Business Media, 2012.<\/li>\n<li>WALTL, Markus. \u201cEnriching multimedia with sensory effects: annotation and simulation tools for the representation of sensory effects\u201d. VDM Verlag, 2010.<\/li>\n<li>HALSALL, Fred. \u201cMultimedia Communications: Applications, networks, protocols, and standards\u201d. Pearson Education, 2001.<\/li>\n<\/ol>\n<p><strong>T\u00f3picos Especiais em Otimiza\u00e7\u00e3o<\/strong><\/p>\n<p>Abordagens atrav\u00e9s de m\u00e9todos exatos para a solu\u00e7\u00e3o de problemas de programa\u00e7\u00e3o linear e n\u00e3o-linear; implementa\u00e7\u00e3o de heur\u00edsticas e metaheur\u00edsticas para a solu\u00e7\u00e3o de problemas, de grande porte, em diversas \u00e1reas de aplica\u00e7\u00e3o.<\/p>\n<ol>\n<li>Glover, F., Kochenberger, G.A., Handbook of Metaheristics, Kluwer Academic Publishers, 2002.<\/li>\n<\/ol>\n<p dir=\"ltr\"><strong>T\u00f3picos Especiais em Programa\u00e7\u00e3o<\/strong><\/p>\n<div>Desenvolvimento de prot\u00f3tipos ou artefatos computacionais. Tend\u00eancias e inova\u00e7\u00f5es da \u00e1rea de programa\u00e7\u00e3o.<\/div>\n<ol>\n<li>Raul Wazlawick. Metodologia de Pesquisa para Ci\u00eancia da Computa\u00e7\u00e3o. Elsevier, 2 edi\u00e7\u00e3o, September 2014.<\/li>\n<li>Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C., Algoritmos: teoria e pr\u00e1tica. Tradu\u00e7\u00e3o da 3\u00aa Edi\u00e7\u00e3o Americana, Elsevier Editora LTDA, 2012.<\/li>\n<li>Programming Languages: Principles and Paradigms, Maurizio Gabbrielli, Simone Martini, Springer, 2010.<\/li>\n<\/ol>\n<p id=\"m_-1688091767521969762gmail-m_1224512105397366043gmail-docs-internal-guid-e0fde0a6-7fff-5fe3-8d98-7ee4b88e5384\" dir=\"ltr\"><strong>Visualiza\u00e7\u00e3o de Dados<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Fundamentos de visualiza\u00e7\u00e3o de dados, a percep\u00e7\u00e3o de vari\u00e1veis discretas e cont\u00ednuas, modelos de visualiza\u00e7\u00e3o, gr\u00e1ficos din\u00e2micos, modelo de visualiza\u00e7\u00e3o de agrupamentos. <\/span><span style=\"font-weight: 400;\">A disciplina inclui a concep\u00e7\u00e3o e o desenvolvimento de representa\u00e7\u00f5es visuais e complementares de modo a apoiar a resposta \u00e0 perguntas, tomada decis\u00f5es e percep\u00e7\u00e3o de evid\u00eancias apoiadas pelos dados, sendo uma ferramenta de suporte \u00e0 An\u00e1lise de Dados (<\/span><i><span style=\"font-weight: 400;\">Data Analytics<\/span><\/i><span style=\"font-weight: 400;\">).<\/span><\/p>\n<ol>\n<li>Thomas A. Runkler<span style=\"font-weight: 400;\">. Data Analytics: Models and Algorithms for Intelligent Data Analysis. Vieweg+Teubner Verlag, Wiesbaden; New York, 2012 edition, September 2012.<\/span><\/li>\n<li>Alexandru C. Telea<span style=\"font-weight: 400;\">. Data Visualization: Principles and Practice, Second Edition. A K Peters\/CRC Press, Boca Raton, 2 edition, September 2014.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Andy Kirk. Data Visualization: a successful design process. Packt Publishing, Birmingham, December 2012.<\/span><\/li>\n<li><span style=\"font-weight: 400;\"> Nathan Yau. Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley, Indianapolis, Ind, 1 edition, July 2011.<\/span><\/li>\n<li><span style=\"font-weight: 400;\"> Nathan Yau. Data Points: Visualization That Means Something. Wiley, 1 edition, April 2013.<\/span><\/li>\n<li><span style=\"font-weight: 400;\"> Alex Wright. Big Data Meets Big Science. Commun. ACM, 57(7):13\u201315, July 2014.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Katy B \u0308orner and David E. Polley. Visual Insights: A Practical Guide to Making Sense of Data. The MIT Press, Cambridge, Massachussetts, January 2014.<\/span><\/li>\n<li><span style=\"font-weight: 400;\"> Leland Wilkinson, D. Wills, D. Rope, A. Norton, and R. Dubbs. The Grammar of Graphics. Springer, New York, 2nd edition, July 2005.<\/span><\/li>\n<\/ol>\n<p><strong>Estudo Orientado<\/strong><\/p>\n<p>A disciplina \u00e9 destinada ao desenvolvimento de tarefas espec\u00edficas focadas no tema de pesquisa dos(as) discentes do Programa. A inscri\u00e7\u00e3o na disciplina Estudo Orientado dever\u00e1 ser feita com o docente respons\u00e1vel pela orienta\u00e7\u00e3o &#8211; orientador(a) principal &#8211; do(a) discente, e poder\u00e1 ser renovada em per\u00edodos letivos subsequentes ou n\u00e3o at\u00e9 a defesa de proposta de disserta\u00e7\u00e3o. Ser\u00e1 permitido a obten\u00e7\u00e3o de, no m\u00e1ximo, 6 cr\u00e9ditos com a disciplina Estudo Orientado.<\/p>\n<p><strong>Pesquisa para Disserta\u00e7\u00e3o de Mestrado<\/strong><\/p>\n<p>O(A) aluno(a), ap\u00f3s ter integralizado os 24 cr\u00e9ditos, deve se inscrever na disciplina Pesquisa para Disserta\u00e7\u00e3o de Mestrado. O objetivo da disciplina \u00e9 a elabora\u00e7\u00e3o da Disserta\u00e7\u00e3o de Mestrado. A disciplina n\u00e3o tem atribui\u00e7\u00e3o de cr\u00e9dito conforme especificado no projeto.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>O(A) aluno(a) do PPCIC dever\u00e1 integralizar as disciplinas obrigat\u00f3rias de An\u00e1lise e Projeto de Algoritmos e de Pesquisa para Disserta\u00e7\u00e3o de Mestrado, bem como atingir um total de cr\u00e9ditos igual ou superior a 24 (vinte e quatro). , assim distribu\u00eddos: 3 (tr\u00eas) cr\u00e9ditos na disciplina obrigat\u00f3ria de An\u00e1lise e Projeto de Algoritmos m\u00ednimo de 6 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-357","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/eic.cefet-rj.br\/ppcic\/wp-json\/wp\/v2\/pages\/357","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/eic.cefet-rj.br\/ppcic\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/eic.cefet-rj.br\/ppcic\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/ppcic\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/ppcic\/wp-json\/wp\/v2\/comments?post=357"}],"version-history":[{"count":137,"href":"https:\/\/eic.cefet-rj.br\/ppcic\/wp-json\/wp\/v2\/pages\/357\/revisions"}],"predecessor-version":[{"id":6243,"href":"https:\/\/eic.cefet-rj.br\/ppcic\/wp-json\/wp\/v2\/pages\/357\/revisions\/6243"}],"wp:attachment":[{"href":"https:\/\/eic.cefet-rj.br\/ppcic\/wp-json\/wp\/v2\/media?parent=357"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}