Introdução à Machine Learning

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Introdução à Machine Learning

  1. 1. Aprendizado de M´quina e Grandes Conjuntos de Dados a Prof. Dr. Thomas de Araujo Buck September 12, 2011Contents1 Tipos de algoritmos 2 1.1 Determin´ısticos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ´ 1.1.1 ”Arvore de jogos” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Adaptativos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2.1 Alguns exemplos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 A enorme avalanche de dados 9 2.1 Data centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Tratamento dos dados . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Aprendizado de M´quina a 13 3.1 Tarefa t´ ıpica de data mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2 Problemas muito dif´ ıceis para serem programados . . . . . . . . . . . . . . . . . . . . . . . 15 3.3 Software that customizes to user . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Grandes conjuntos de dados 18 4.1 Outros temas correlatos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2 Exemplos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2.1 KDD (com SVM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2.2 Imagens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2.3 V´ ıdeos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 ´ 4.2.4 Area m´dica . . . . e . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Conclus˜es o 24 1
  2. 2. 1 Tipos de algoritmos 1. Determin´ ısticos (ou cl´ssicos, convencionais) a 2. Adaptativos (ou estoc´sticos, ”avan¸ados”) a c1.1 Determin´ ısticos • Detec¸˜o de colis˜o ca a • Fatora¸˜o de n´meros primos ca u • Invers˜o de matrizes (esparsas) a • Ordena¸˜o (quicksort, mergesort) ca • Page Rank • Um pouco mais avan¸ados c – A* ´ – ”Arvore de jogos” 2
  3. 3. 1.1.1 ´ ”Arvore de jogos” • Jogo da velha – Qual a quantidade total de possibilidades? ∗ 9 × 8 × . . . × 1 = 9! = 362.880 • Jogo de damas 3
  4. 4. • Xadrez [5, 6] 4
  5. 5. • Qual a quantidade total de possibilidades?1 – Se for considerado uma profundidade P, e ramifica¸˜o R, a quantidade poss´ de n´s N pode ca ıvel o ser calculado com a f´rmula o N = RP – O tamanho m´dio de uma partida de xadrez ´ de 50 lances, ou seja, 100 jogadas, sendo 50 e e jogadas realizadas pelas pe¸as brancas e 50 pelas pe¸as negras. c c – Como o fator de ramifica¸˜o ´ em m´dia de 35, pode-se ent˜o estimar a quantidade de n´s de ca e e a o uma ´rvore correspondente a uma partida, como sendo N = 35100 = 2, 55155207 ∗ 10154 . a – Caso um computador percorra dois milh˜es de posi¸˜es por segundo, seriam necess´rios mais o co a de 5, 3 ∗ 10109 anos para esgotar toda a ´rvore. a • Surge ent˜o a famosa pergunta: o que ´ um programa ”inteligente” ? a e • Quem se lembra da disputa homem (Garry Kasparov) contra m´quina (IBM Deep Blue) [7, 8] ? a • Mais uma pergunta: xadrez ´, neste sentido, o jogo mais ”dif´ e ıcil” j´ criado pelo homem? a1 Resposta obtida na internet. 5
  6. 6. • Go [11]• Ver tamb´m [9, 10] e• H´ sinais de esperan¸a [12] a c 6
  7. 7. 1.2 Adaptativos • O que ´ um programa ”inteligente”? e ´ • E um programa ”que aprende”?1.2.1 Alguns exemplos • Reconhecimento de face • An´lise de cr´dito a e • Navega¸˜o autˆnoma ca o • Diagn´stico m´dico o e • Proje¸˜o financeira (progn´stico) ca o • Sistemas de recomenda¸˜o ca • Log´ ıstica 7
  8. 8. • Text processing – Spam – News – Pl´gio a• Aprendizado de m´quina a – Supervisionado (aprende com exemplos), que possui 2 fases: treinamento e opera¸˜o ca ∗ NN ∗ Classifica¸˜o (Discriminante Linear - DL) ca ∗ Regress˜o [66, 67] a – N˜o supervisionado (aprende sozinho), que s´ possui a fase de opera¸˜o a o ca ∗ An´lise de aglomera¸˜o (K-means clustering) a ca 8
  9. 9. 2 A enorme avalanche de dados 9
  10. 10. • Mat´ria da revista The Economist [4] e 10
  11. 11. 11
  12. 12. 2.1 Data centers • Google [73] • Facebook [72]2.2 Tratamento dos dados • O que fazer com esses dados? Apenas armazenar? Indexar? • Ou deve-se extrair informa¸˜o util? ca ´ 12
  13. 13. 3 Aprendizado de M´quina a • Defini¸˜o de Machine Learning (ML): ver [38] ca • Outra defini¸˜o de ML: ver [39] ca • Sobre Support Vector Machines (SVM): ver [38, 51] – Support vector machines represent a powerful new class of models invented by Vladimir Vapnik in the early 1990s 13
  14. 14. • 3 exemplos de aplica¸˜es de ML [39] co3.1 Tarefa t´ ıpica de data mining • An´lise de risco de cr´dito a e 14
  15. 15. 3.2 Problemas muito dif´ ıceis para serem programados • A competi¸˜o DARPA Grand Challenge: vers˜o urbana [42, 43, 44, 45] ca a • A experiˆncia Google Car [41] e 15
  16. 16. • Mais alguns detalhes• Um pequeno problema?• Outras referˆncias [52, 54, 55] e 16
  17. 17. 3.3 Software that customizes to user 17
  18. 18. 4 Grandes conjuntos de dados • An´lise de dados a – Manual – Autom´tica a4.1 Outros temas correlatos • Data mining – Manual ∗ Visual data mining [63] – Autom´tica a4.2 Exemplos • An´lise de risco de cr´dito a e • A experiˆncia IBM Watson [40, 46, 47] e 18
  19. 19. 4.2.1 KDD (com SVM) • Ver [38] 19
  20. 20. 4.2.2 Imagens • Acesso por conte´do [13, 14, 15, 16, 17, 20, 21, 24] u • PhotoLib [19] • Games with a purpose (GWAP) [18, 26] • Pixazza → Luminate • Semantics [22, 23] • Learning [23, 25]4.2.3 V´ ıdeos • An´lise a 20
  21. 21. 4.2.4 ´ Area m´dica e • Mamografia • Colonoscopia [30, 31, 35] – As gera¸˜es dos equipamentos de tomografia computadorizada co – Tipos: convencional e ”virtual” - vantagens e inconvenientes / limita¸˜es co – Visualiza¸˜o simples [29] ca 21
  22. 22. • Display modes for CT colonography [32, 33]• Computer-Aided Diagnosis (CAD): detecting polyps at CT colonography [34] 22
  23. 23. • Quantification of Distention in CT Colonography [36]• Computerized Detection of Colonic Polyps at CT Colonography [37] 23
  24. 24. 5 Conclus˜es o • Tratamento computacional de grandes quantidades de dados ´ uma oportunidade, segundo a con- e sultoria McKinsey [27, 28] 24
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