An Introductory presentation on Machine Learning and Apache Mahout. I presented it at the BigData Meetup - Pune Chapter's first meetup (http://www.meetup.com/Big-Data-Meetup-Pune-Chapter/).

1. + Machine
Learning
and
Apache
Mahout
Varad Meru
Software Development Engineer
Orzota, Inc.
about.me/vrdmr
© Varad Meru, 2013

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Who Am I

Orzota, Inc.



Making BigData Easy
Designing a Cloud-based platform for ETL, Analytics
Past Work Experience


Persistent Systems Ltd.
Recommendation Engines and User Behavior Analytics.
Area of Interest

Machine Learning

Distributed Systems

Recommendation Engines

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Outline

Introduction

Machine Learning





Apache Mahout




Introduction and History
Types of Learning Algorithms
Applications
What’s New
History
Architecture
Applications and Examples
Conclusion
© Varad Meru, 2013

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Machine Learning
Rise of the Machine-Era
4

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Introduction
“Machine Learning is Programming Computers to
optimize a Performance Criterion using Example Data
or Past Experience”

Term coined by Arthur Samuel

"Field of study that gives computers the ability to learn without being
explicitly programmed“.

Branch of Artificial Intelligence and Statistics

Focuses on prediction based on known properties

Used as a sub-process in Data Mining.

Data Mining focuses on discovering new, unknown properties.

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Learning Algorithms

Supervised Learning



Unsupervised Learning



Unlabelled input data.
Creating a function to predict the relation and output
Semi-Supervised Learning


Labelled input data.
Creating classifiers to predict unseen inputs.
Combines Supervised and Unsupervised Learning methodology
Reinforcement Learning

Reward-Punishment based agent.

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Supervised Learning
Introduction

Learn from the Data

Data is already labelled


Expert, Crowd-sourced or case-based labelling of data.
Applications

Handwriting Recognition

Spam Detection

Information Retrieval

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Personalisation based on ranks
Speech Recognition

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Supervised Learning
Algorithms

Decision Trees

k-Nearest Neighbours

Naive Bayes

Logistic Regression

Perceptron and Multi-level Perceptrons

Neural Networks

SVM and Kernel estimation

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Supervised Learning
Example: Naive Bayes Classifier

President Obama’s Speech’s Word Map

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Supervised Learning
Example: Naive Bayes Classifier

A Spam Document’s Word Map

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Supervised Learning
Example: Naive Bayes Classifier

Running a test on the Classifier
“Order a trial Adobe
chicken daily EABList new summer
savings, welcome!”
Classifier
Spam
Bin

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Unsupervised Learning
Introduction

Finding hidden structure in data

Unlabelled Data

SMEs needed post-processing to verify, validate and use the
output

Used in exploratory analysis rather than predictive analytics

Applications

Pattern Recognition

Groupings based on a distance measure

Group of People, Objects, ...

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Unsupervised Learning
Algorithms

Clustering

k-Means, MinHash, Hierarchical Clustering

Hidden Markov Models

Feature Extraction methods

Self-organizing Maps (Neural Nets)

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Unsupervised Learning
Example K-Means
Source: http://apandre.wordpress.com/visible-data/cluster-analysis/

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Learning Problem
Cat and Dog Problem

Humans can easily classify which is a cat and which is a dog.

But how can a computer do that?

Some attempts used Clustering Mechanisms to solve it – Cooccurence Clustering, Deep Learning

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Apache Mahout
Scalable Machine Learning Library
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© Varad Meru, 2013

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History and Etymology

Inspired from MapReduce for Machine
Learning on Multicore” Ng et. al.

Written in Java. Apache License.

Founders

Mahout – Isabel Drost, Grant Ingersoll, Karl
Witten.
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Taste – Sean Owen
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Mahout – Keeper/Driver of Elephants.
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Current Release – 0.8 (stable)
© Varad Meru, 2013

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Size
Need

BigData

Ever-growing data.

Yesterday’s methods to
process tomorrow’s data


Cheap Storage
Scalable from Ground Up


Lines
Sample
Data
KBs –
low MBs
Prototype
Data
Analysis and
Visualisation
Analysis and
Visualisation
Tools18
Whiteboard,
Bash, ...
Matlab,
Octave, R,
Processing,
Bash, ...
Storage
MySQL (DBs),
...
Analysis
NumPy, SciPy,
Pandas,
Weka..
MBs – low
GBs
Should be build on top of anyOnline
existing Distributed Systems Data
framework
Should contain distributed
version of ML algorithms
Classification
GBs
– TBs
– PBs
Visualisation
Flare,
AmCharts,
Raphael
Storage
HDFS, Hbase,
Cassandra,...
Analysis
Hive, Giraph,
Hama, Mahout

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Mahout Modules
Applications
Evolutionary
Algorithms
Classification
Utilies
Lucene/Vectorizer
Clustering
Recommenders
Math
Vectors/ Matrics/SVD
Regression
Collections
(Primitives)
FPM
Dimension
Reduction
Hadoop

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Recommender
Systems
© Varad Meru, 2013

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Recommender Systems
Introduction

Types of Recommender Systems



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Content Based Recommendations
Collaborative Filtering Recommendations
 User-User Recommendations
 Item-Item Recommendations
Dimensionality Reduction (SVD) Recommendations
Applications

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Products you would like to buy
People you might want to connect with
Potential Life-Partners
Recommending Songs you might like
...

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Recommender Systems
Collaborative Filtering in Action

Assuming people
have seen at least
one movie.
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Cold Start?


© Varad Meru, 2013
1: seen
0: not seen

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Collaborative Filtering in Action

Tanimoto Coefficient
T ( a, b)
NA
NC
NB
NC

NA – Number of Customers
who bought A

NB – Number of Customers who
bought B

NC – Number of Customers
who bought A and B
© Varad Meru, 2013

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Collaborative Filtering in Action

Cosine Coefficient
C (a, b)
NC
NA
NB

NA – Number of Customers
who bought A

NB – Number of Customers who
bought B

NC – Number of Customers
who bought A and B
© Varad Meru, 2013

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Apache Mahout
Recommender System
Architecture

Two Modes



Stand-alone non distributed (“Taste”)
Scalable Distributed Algorithmic version
for Collaborative Filtering
Top-level Packages

Data Model

User Similarity

Item Similarity

User Neighbourhood

Recommender

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Naive Bayes Classifier
“Order a trial Adobe
chicken daily EABList new summer
savings, welcome!”
Classifier

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Naive Bayes Classifier

Naive Bayes is a pretty complex process in Mahout: training
the classifier requires four separate Hadoop jobs.

Training:


Calculate per-Document
Statistics

Normalize across Categories


Read the Features
Calculate normalizing factor
of each label
Testing

Classification (fifth job, explicitly invoked)
© Varad Meru, 2013

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K-Means Clustering
Iterations

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K-Means Clustering
MapReduce Version

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Summary
•
Machine Learning
•
•
•
Learning Algorithms
Varied Applications
Mahout
•
Scaling to Giga/Tera/Peta Scale
•
Free and Open Source

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More Info.
1.
“Scalable Similarity-Based Neighborhood Methods with
MapReduce” by Sebastian Schelter, Christoph Boden and
Volker Markl. – RecSys 2012.
2.
“Case Study Evaluation of Mahout as a Recommender Platform”
by Carlos E. Seminario and David C. Wilson - Workshop on
Recommendation Utility Evaluation: Beyond RMSE (RUE 2012)
3.
http://mahout.apache.org/ - Apache Mahout Project Page
4.
http://www.ibm.com/developerworks/java/library/j-mahout/ Introducing Apache Mahout
5.
[VIDEO] “Collaborative filtering at scale” by Sean Owen
6.
[BOOK] “Mahout in Action” by Owen et. al., Manning Pub.
© Varad Meru, 2013

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Questions?
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© Varad Meru, 2013

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Thank You
Go BigData!!! 
© Varad Meru, 2014