1. Machine Learning
with Apache Mahout

2. http://twitter.com/danielglauser
http://www.linkedin.com/in/danglauser
danglauser@gmail.com

3. What is Machine
Learning?

4. What is Machine
Learning?
A branch of Artificial Intelligence

5. What is Machine
Learning?
A branch of Artificial Intelligence
Creative use of statistics

6. What is Machine
Learning?
A branch of Artificial Intelligence
Creative use of statistics
Smart decisions from large data sets

7. What is Machine
Learning?
A branch of Artificial Intelligence
Creative use of statistics
Smart decisions from large data sets
All of the above

8. Common Applications

9. Common Applications
?

10. Spam
Filtering

11. Credit
Card
Fraud

12. Medical Diagnostics

13. Search Engines

14. Sentiment Analysis

15. Math Alert
If you want to go big with Machine Learning
math is necessary
What math?

17. Statistics
Discrete Math
Linear Algebra
Probability

21. Apache Mahout
A platform for Machine Learning
Roll your own algorithm, use the platform
Easy integration with Hadoop

22. History
• 2005 The Taste framework
• 2008 Services built on Lucene

24. Mahout is composed
of...
Recommender Engines
Classification
Clustering
Frequent itemsets

25. A brief intro to:
Recommender Engines
Classification
Clustering

26. Recommendations
For a given set of input, make a
recommendation

27. Recommendations
Rank the best out of many possibilities

28. Recommenders are
typically
User based
or
Item based

29. Neighborhood
Nearest N Users Threshold

30. Similarity

31. PearsonCorrelationSimilarity
Produces a value between 1 and -1
Tendency of two series to move together

32. PearsonCorrelationSimilarity
1 - the two series are similar
0 - no similarity
-1 - opposite similarity

33. PearsonCorrelationSimilarity
Problems
Doesn’t take into account how many items
overlap between users
Cannot find similarity between two users if
they only have one item in common
Undefined if two users have identical
preferences

34. Similarity Algorithms
PersonCorrelationSimilarity
EuclidianDistanceSimilarity
TanimotoCoefficientSimilarity
LogLikelyhoodSimilarity

36. To the code!

37. How big is a Java Object?

38. GenericPreference
user id - long - 8 bytes
item id - long - 8 bytes
preference value - float - 4 bytes

39. PreferenceArray
Why not just use an array or an ArrayList?
A little overhead x millions of items =
a *lot* of overhead

40. GenericUserPreferenceArray
item id - long - 8 bytes
preference value - float - 4 bytes
] x millions
-
one user id - long - 8 bytes

41. Phew!

42. Clustering

43. Clustering

44. Clustering
Surface naturally occurring groups of data
A notion of similarity (and dissimilarity)

45. Clustering
Algorithms do not require training
Stopping condition - iterate until close
enough

46. Common Clustering
Algorithms
K-Means
Fuzzy K-Means
Meanshift
Centroid generation
Direchlet clustering

47. Representing Data
Feature Selection
Vectorization

48. Feature Selection
Figure out what features of your data are
interesting

49. Vectorization
Represent the interesting features in an n-
dimensional space

50. N-Dimensional Space
Every word in a group of documents
Size, shape, color of an object

51. N-Dimensional Space
Every word in a group of documents
Size, shape, color of an object

52. Representing Vectors
DenseVector
RandomAccessSparseVector
SequentialAccessSparseVector

53. Representing Vectors
DenseVector
Random Seek
RandomAccessSparseVector
SequentialAccessSparseVector

54. Hadoop SequenceFiles
Input vectors SequenceFile(s)
Initial
SequenceFile(s)
Centoids

55. K-Means
50+ years old, in commonly used for 25 years
Set the number of clusters - k
Works well even if you don’t pick a good - k

56. K-Means
Guess at initial placement of the centers (centroids)
]-
Expectation - assign the nearest Wash,
points to each centroid rinse,
Maximization - reposition the centroid repeat

57. C1
C2
C3

58. C1
C2
C3

59. C1
C1
C2
C3
C3
C2

60. C1
C3
C2

61. C1
C3
C2

62. C1
C1
C3
C3
C2
C2

63. C1
C3
C2

64. C1
C3
C2

65. C1
C1
C3
C3
C2
C2

66. C1
C3
C2

67. C1
C3
C2

68. C1
C1
C3
C3
C2
C2

69. Stop!
C1
C3
C2

70. Clustering

71. Clustering

72. Classification

73. Classification

74. Classification

75. Classification
BFF39D 577335 B3E631 D0F5B0 90B073 AFCF3C

76. Classification
BFF39D 577335 B3E631 D0F5B0 90B073 AFCF3C

77. Classification
BFF39D 577335 B3E631 D0F5B0 90B073 AFCF3C
Green

78. Attributes of Classification
Algorithms
Require training (supervised)
Make a single decision with a very limited set
of outcomes

79. Classification
Typical answers naturally fit into categories

80. Examples of Classification
Credit card fraud prediction
Customer attrition
Diabetes detector
Search Engine

81. Training - learned process that produces a model
Model - output of the training algorithm

82. Predictor variable - input for classification model
Target variable - what we are trying to predict

83. Classification

84. Common Algorithms
Stochastic Gradient Decent (SGD)
Support Vector Machine (SVM)
Naive Bayes
Complementary Naive Bayes
Random Forrest

85. Going Distributed

86. Overhead
Parallel processing requires management
overhead
Especially when spread over multiple machines

88. Vector SequenceFile
Keys Values
Implements Implements
WritableComparable Writeable

89. Java
WritableComparable Comparable
Writeable Serializable

90. Recap

91. Recommender
Rank large datasets

92. Clustering
Group your data

93. Classification
Train me to think like you

94. Integration with
Hadoop
Through SequenceFiles and Map/Reduce jobs

95. Resources

96. Resources

101. n-dimensional space http://en.wikipedia.org/wiki/File:Coord_system_CA_0.svg
Batman http://www.flickr.com/photos/farukahmet/3005752670/sizes/l/in/photostream/
duke http://kenai.com/projects/duke/pages/Home
mahout logo http://mahout.apache.org/
scalability diagram http://manning.com/owen/
Thanks!
classification diagram http://manning.com/owen/
phew http://www.flickr.com/photos/iain/1022210850/
clouds http://www.flickr.com/photos/spazzo_1493/3682989696/
spam http://www.flickr.com/photos/johotravels/4334224546/
credit card http://www.flickr.com/photos/thetruthabout/4542026865/
medical diagnostics http://www.flickr.com/photos/adrianclarkmbbs/3063516728/
search engines http://www.flickr.com/photos/enda/144377951/
angry http://www.flickr.com/photos/jmgasalla/3467458535/
crystal ball http://www.flickr.com/photos/mache/142561526/
glasses http://www.flickr.com/photos/nickwheeleroz/2220008689/
coffee http://www.flickr.com/photos/mr_t_in_dc/2818254382/

102. http://twitter.com/danielglauser
http://www.linkedin.com/in/danglauser
danglauser@gmail.com