1. Introducing Apache Mahout
Scalable Machine Learning for All!
Grant Ingersoll
Lucid Imagination

2. Overview
What is Machine Learning?
Mahout

3. Definition
“Machine Learning is programming computers to optimize a performance criterion using example data or past experience”
Intro. To Machine Learning by E. Alpaydin
Subset of Artificial Intelligence
Many other fields: comp sci., biology, math, psychology, etc.

4. Types Supervised Unsupervised Semi-Supervised
Using labeled training data, create function that predicts output of unseen inputs
Unsupervised
Using unlabeled data, create function that predicts output
Semi-Supervised
Uses labeled and unlabeled data

5. Characterizations Lots of Data Identifiable Features in that Data
Too big/costly for people to handle
People still can help

6. Clustering Unsupervised Find Natural Groupings Documents
Search Results
People
Genetic traits in groups
Many, many more uses

7. Example: Clustering
Google News

8. Collaborative Filtering
Unsupervised
Recommend people and products
User-User
User likes X, you might too
Item-Item
People who bought X also bought Y

9. Example: Collab Filtering
Amazon.com

10. Classification/Categorization
Many, many types
Spam Filtering
Named Entity Recognition
Phrase Identification
Sentiment Analysis
Classification into a Taxonomy

11. Example: NER
NER?
Excerpt from Yahoo News

12. Example: Categorization

13. Info. Retrieval Learning Ranking Functions
Learning Spelling Corrections
User Click Analysis and Tracking

14. Other Image Analysis Robotics Games
Higher level natural language processing
Many, many others

15. What is Apache Mahout?
A Mahout is an elephant trainer/driver/keeper, hence… +
Machine Learning =
(and other distributed techniques)

16. What? Hadoop brings: Mahout brings: Map/Reduce API HDFS
In other words, scalability and fault-tolerance
Mahout brings:
Library of machine learning algorithms
Examples

17. Why Mahout? Many Open Source ML libraries either: Lack Community
Lack Documentation and Examples
Lack Scalability
Lack the Apache License ;-)
Or are research-oriented

18. Why Mahout? Intelligent Apps are the Present and Future
Thus, Mahout’s Goal is:
Scalable Machine Learning with Apache License

19. Current Status What’s in it: Simple Matrix/Vector library
Taste Collaborative Filtering
Clustering
Canopy/K-Means/Fuzzy K-Means/Mean-shift/Dirichlet
Classifiers
Naïve Bayes
Complementary NB
Evolutionary
Integration with Watchmaker for fitness function

20. How?
Examples
Taste
Clustering
Classification
Evolutionary

21. Taste: Movie Recommendations
Given ratings by users of movies, recommend other movies

22. Taste Demo
mvn jetty:run-war

23. Clustering: Synthetic Control Data
Each clustering impl. has an example Job for running in <MAHOUT_HOME>/examples
o.a.mahout.clustering.syntheticcontrol.*
Outputs clusters…
See output.txt, synthetic_control data

24. Classification: NB and CNB Examples
20 Newsgroups
Wikipedia

25. Evolutionary Traveling Salesman Class Discovery
Class Discovery

26. What’s Next? More Examples Winnow/Perceptron (MAHOUT-85)
Text Clustering
Association Rules (MAHOUT-108)
Logistic Regression
Solr Integration (SOLR-769)
GSOC

27. When, Who When? Now! Who? You! We want others to: Mahout is growing
We want programmers who:
Are comfortable with math
Like to work on hard problems
We want others to:
Kick the tires

28. Where? http://lucene.apache.org/mahout http://cwiki.apache.org/MAHOUT
Hadoop -

29. Resources “Programming Collective Intelligence” by Segaran
“Data Mining - Practical Machine Learning Tools and Techniques” by Witten and Frank
“Taming Text” by Ingersoll and Morton
Taming Text – Open source tools for doing machine learning