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

2. Agenda What is Machine Learning? Mahout Definitions Types Applications
Why?
How?
Who?

3. NOT! What is Machine Learning? Or?

4. How about?
Google News

5. Or?
Amazon.com

6. 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.

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

8. 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

9. Classification/Categorization
Spam Filtering
Named Entity Recognition
Phrase Identification
Sentiment Analysis
Classification into a Taxonomy

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

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

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

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

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

15. What? Hadoop brings: Thus, Mahout’s Goal is: Map/Reduce API HDFS
In other words, scalability and fault-tolerance
Thus, Mahout’s Goal is:
Scalable Machine Learning with Apache License

16. Why Mahout? Many Open Source ML libraries either:
Lack Community
Lack Documentation and Examples
Lack Scalability
Lack the Apache License ;-)
Or are research-oriented
Personal: Learn more ML
Intelligent Apps are the Present and Future
See the Hadoop talks tomorrow and Friday!
Goal: Overcome gaps the Apache Way!

17. Current Status Close to Initial release What’s in it:
Focused on examples, docs, bug fixes
What’s in it:
Simple Matrix/Vector library
Taste Collaborative Filtering
Clustering
Canopy/K-Means/Fuzzy K-Means/Mean-shift
Classifiers
Naïve Bayes
Complementary NB
Evolutionary
Integration with Watchmaker for fitness function

18. How?
Examples
Taste
Clustering
Classification
Evolutionary

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

20. 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

21. Classification: NB and CNB Examples
20 Newsgroups
Wikipedia

22. Evolutionary Traveling Salesman Class Discovery
Class Discovery

23. What’s Next? Release 0.1! Shared Amazon Images (others?) More Examples
Winnow/Perceptron (MAHOUT-85)
Hbase and HAMA support
Normalize I/O format for data
Solr Integration (SOLR-769)
Other Algorithms: SVM, Linear Regression, etc.

24. When, Where, Who When? Now! Who? You! Where? Mahout is growing
We want Java programmers who:
Are comfortable with math
Like to work on large, hard problems
Where?

25. Resources “Programming Collective Intelligence” by Toby Segaran
“Data Mining - Practical Machine Learning Tools and Techniques” by Ian H. Witten and Eibe Frank
Hadoop -