This document discusses machine learning and large data sets. It covers types of algorithms, including deterministic and adaptive models. It describes the enormous growth of digital data and challenges of data analysis. Machine learning is defined, and applications like credit analysis, autonomous vehicles and medical diagnosis are mentioned. Analyzing large data sets through manual and automated methods like data mining is also discussed. Examples of large data set analysis include images, videos and medical areas like mammography and colonoscopy. The conclusion is that computational analysis of large amounts of data presents opportunities.

1. Aprendizado de M´quina e Grandes Conjuntos de Dados
a
Prof. Dr. Thomas de Araujo Buck
September 12, 2011
Contents
1 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 A enorme avalanche de dados 9
2.1 Data centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Tratamento dos dados . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5 Conclus˜es
o 24
1

2. 1 Tipos de algoritmos
1. Determin´
ısticos (ou cl´ssicos, convencionais)
a
2. Adaptativos (ou estoc´sticos, ”avan¸ados”)
a c
1.1 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. 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. • Xadrez [5, 6]
4

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?
a
1 Resposta obtida na internet.
5

6. • Go [11]
• Ver tamb´m [9, 10]
e
• H´ sinais de esperan¸a [12]
a c
6

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. • 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. 2 A enorme avalanche de dados
9

10. • Mat´ria da revista The Economist [4]
e
10

11. 11

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. 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. • 3 exemplos de aplica¸˜es de ML [39]
co
3.1 Tarefa t´
ıpica de data mining
• An´lise de risco de cr´dito
a e
14

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. • Mais alguns detalhes
• Um pequeno problema?
• Outras referˆncias [52, 54, 55]
e
16

17. 3.3 Software that customizes to user
17

18. 4 Grandes conjuntos de dados
• An´lise de dados
a
– Manual
– Autom´tica
a
4.1 Outros temas correlatos
• Data mining
– Manual
∗ Visual data mining [63]
– Autom´tica
a
4.2 Exemplos
• An´lise de risco de cr´dito
a e
• A experiˆncia IBM Watson [40, 46, 47]
e
18

19. 4.2.1 KDD (com SVM)
• Ver [38]
19

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. 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. • Display modes for CT colonography [32, 33]
• Computer-Aided Diagnosis (CAD): detecting polyps at CT colonography [34]
22

23. • Quantification of Distention in CT Colonography [36]
• Computerized Detection of Colonic Polyps at CT Colonography [37]
23

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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