1. The document summarizes a presentation about Apache Mahout, an open source machine learning library. It discusses algorithms like clustering, classification, topic modeling and recommendations. 2. It provides an overview of clustering Reuters documents using K-means in Mahout and demonstrates how to generate vectors, run clustering and inspect clusters. 3. It also discusses classification techniques in Mahout like Naive Bayes, logistic regression and support vector machines and shows code examples for generating feature vectors from data.

1. Hands on! Speakers: Ted Dunning, Robin Anil OSCON 2011, Portland

7. Algorithms and Applications

12. Part 1 - Clustering

16. Clustering Reuters dataset

24. K-Means clustering c1 c2 c3

25. K-Means clustering c1 c2 c3

26. K-Means clustering c1 c2 c3 c1 c2 c3

27. K-Means clustering c1 c2 c3

34. Part 2 - Classification

37. Data Flow Not quite so simple

40. Data Flow

42. The pipeline Classifiable Data Vectors

43. Instance and Target Variable

44. Instance and Target Variable

45. Hashed Encoding

46. What about collisions?

47. Let’s write some code (cue relaxing background music)

49. K-means using target

50. K-means features

51. More code! (cue relaxing background music)

53. Parallel Stochastic Gradient Descent Average models Train sub model Model I n p u t

54. Variational Dirichlet Assignment Update model Gather sufficient statistics Model I n p u t

57. Modeling architecture Feature extraction and down sampling I n p u t Side-data Data join Sequential SGD Learning Map-reduce Now via NFS

58. More in Mahout