This document outlines an agenda for a machine learning workshop for Java developers. The agenda includes introductions to machine learning algorithms like linear regression, logistic regression, K-nearest neighbors, and K-means clustering. It also discusses machine learning frameworks for Java like Weka, Deeplearning4j, and how to use Jupyter notebooks with Java. The workshop will demonstrate examples using these tools and frameworks.

1. © 2018 IBM Corporation
Eclipse Day
Machine Learning for Java Developers
Nasser Ebrahim(enasser@in.ibm.com)
Software Architect, IBM Software Lab

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Agenda
• Introduction
• Algorithms
• Frameworks in Java
• Demo with Jupyter Notebook & Weka
• Demo with Eclipse & DL4J
• Q & A

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Machine Learning
Machine learning is a field of
computer science that gives
computers the ability to learn
without being explicitly
programmed.
• an application of artificial intelligence
• ability to automatically learn and improve from experience
without being explicitly programmed
• development of computer programs that can access data and
use it learn for themselves

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Machine Learning – Why now?
• Availability of Data
• Storage cost
• Computational power

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Machine Learning – Applications

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Machine Learning – Workflow

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Machine Learning – Features & Label
Features are relevant and independent variables in data (X)
Label is the dependent variable that we need to predict (y)

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Types of Machine Learning

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Supervised Learning
Labeled training data
• Classification
• Regression
Unsupervised Learning
• Clustering
• Association
Unlabeled training data

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Reinforcement Learning

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Linear Regression
In statistics, linear regression is a linear approach for
modelling the relationship between a scalar dependent
variable y and one or more explanatory variables (or
independent variables) denoted X.
y = b0 + b1*X
Best-fit-line that best
describe dataset with
reduced square error

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Logistic Regression
Used when data has binary dependent variables

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K Nearest Neighbors (Classification)
• Based on Euclidian distance of
K points
• K should be greater than
classification groups
• K should always be odd number
for better classification

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K Means clustering
• Works with very large datasets
• Starts by picking k, the number
of clusters
• Start by choosing k random
points – called as centroids
• Populate clusters

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K Means clustering

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Apriori Algorithm - Illustration
• Uses level-wise search, where k-itemsets are used to explore (k+1)
itemsets
• Candidate Generation – Frequent itemsets are extended one at a
time
• Determines frequent itemsets that can be used to determine
association rule which highlight general trends in the database.
• Provides insight into which products tend to be purchased together and
which are most amenable to promotion.
• Trivial pattern
• People who buy chalk-
piece also buy duster
• Inexplicable pattern
• People who buy
mobile also buy bag

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Apriori Algorithm - Example
Min Support = 2
TID Items
100 1 3 4
200 2 3 5
300 1 2 3 5
400 2 5
500 1 3 5
Itemset Support
1 3
2 3
3 4
4 1
5 4
Itemset Support
1 3
2 3
3 4
5 4
CL1 FL1
TID Items
100 1 3 4
200 2 3 5
300 1 2 3 5
400 2 5
500 1 3 5
Itemset Support
1 2 1
1 3 3
1 5 2
2 3 2
2 5 3
3 5 3
Itemset Support
1 3 3
1 5 2
2 3 2
2 5 3
3 5 3
CL2
FL2

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Java ML Libraries & Frameworks
ADAMS
ELKI
Java-ML
JSAT
Encog

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Waikato Environment for Knowledge Analysis (Weka)
• Machine learning/data mining software written in Java (distributed
under the GNU Public License)
• Developed at University of Waikato, New Zealand
• Comprehensive set of data pre-processing tools, learning algorithms
and evaluation methods
• Graphical user interfaces (incl. data visualization)
• Environment for comparing learning algorithms

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Deeplearning4j - DL4J

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Jupyter Notebook
• An open-source web application that allows you to create and
share documents that contain live code, equations, visualizations
and narrative text.
• Install Jupyter using anaconda
• http://jupyter.org/install
• Different kernals to work on
different languages
• python, R, scala, Java, Spark

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Jupyter Notebook with Java Kernal
• IJAVA - Jupyter kernel for executing Java code
• https://github.com/SpencerPark/IJava
• Install IJAVA using the archive from
https://github.com/SpencerPark/IJava/releases/download/v1.1.2/ijava-
1.1.2.zip
• The kernel executes code via the new JShell tool from Java 9.

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Eclipse with maven M2Eclipse
• Launching Maven builds from within Eclipse
• Dependency management for Eclipse build
• Resolving Maven dependencies from the Eclipse workspace
• Wizards for creating new Maven projects, pom.xml, etc
Install Maven in Eclipse
• Eclipse -> Help -> Install New Software
• Enter http://download.eclipse.org/technology/m2e/releases/
• Select “Maven Integration for Eclipse”
• Click on next, accept agreement & Finish

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Q & A