Machine Learning: Neural Networks

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Machine Learning : Neural Networks
NGSACE - SAS

Company Confidential - For Internal Use Only
Copyright © 2016, SAS Institute Inc. All rights reserved.
Global Sales Support & Enablement
AGENDA
2
Machine learning Overview
Introduction to Neural Networks
Industrial Applications of Neural Nets
A walk through demo of Neural Network Applications
SAS – NGASCE

WHAT IS MACHINE LEARNING?
FUN FACT: More than 30 years ago, SAS CEO, Jim Goodnight wrote a procedure for "k-nearest neighbor discriminant
analysis," which is a machine learning method! And growing since….
SAS Data Mining Primer course 1998
Machine
Learning
Machine learning is a branch of
artificial intelligence that
automates the building of systems
that learn iteratively from data,
identify patterns, and predict future
results – with minimal human
intervention.
It shares many approaches with other
related field, but it focuses on predictive
accuracy rather than interpretability of the
model

Automate
• Provide automation to the model building
process by minimizing human
intervention
Customize
• Build powerful models using SAS’s state-
of-the-art algorithms in conjunction with
open source tools
Speed
• Fast response time for sophisticated
analytics applied to data of any size or
complexity
SAS ANALYTICS IN ACTION
Machine Learning

DataMining Machine Learning
TRANSDUCTION
REINFORCEMENT
LEARNING
DEVELOPMENTA
LEARNING
*In semi-supervised learning, supervised prediction and classification algorithms are often combined with clustering.
SEMI-
SUPERVISED
LEARNING
Prediction and
classification*
Clustering*
EM
TSVM
Manifold
regularization
Autoencoders
Multilayer perceptron
Restricted Boltzmann
machines
SUPERVISED
LEARNING
Regression
LASSO regression
Logistic regression
Ridge regression
Decision tree
Gradient boosting
Random forests
Neural
networks
SVM
Naïve Bayes
Neighbors
Gaussian
processes
UNSUPERVISED
LEARNING
A priori rules
Clustering
k-means clustering
Mean shift clustering
Spectral clustering
Kernel density
estimation
Nonnegative
matrix
factorization
PCA
Kernel PCA
Sparse PCA
Singular value
decomposition
SOM

MACHINE LEARNING : WHY IS IT SO IMPORTANT NOW?
Data Computing
Power
Algorithms

Data is the key to driving efficient machine learning modeling: while machine learning relies on algorithms that
learn from data and require less constraints than traditional algorithms, you can use ML tools to efficiently
prepare data for further modeling
Structured Data
Online / Digital Data
Machine Data
Social Media Data

Discovery is the core of machine learning, as machine learning fully exploits the idea of creativity by allowing to
use powerful algorithms to identify patterns and trends in data
Visualization
Prediction
Machine Learning
Optimization

Deployment is a sometimes underestimated capability of the machine learning space, as while the models are
efficiently and powerfully developed, they also need to be deployed for execution in other environments and be
managed for enterprise purposes
Data Warehouse
CRM / Call Center
Mobile Channel
Devices

MACHINE LEARNING PROCESS FLOW
Data
Preprocessing
Feature
Engineering
Learning
Algorithm
Model
Evaluation
Classify/
Predict
Trained Model
Post
Processing
Raw
Data
New Incoming
Data
%
Accuracy

APPLICATIONS OF MACHINE LEARNING
Predictive Asset
Maintenance
Fraud
Credit Scoring
Next Best Offers Customer Segmentation
Targeted Acquisition /
Retention / Attrition
Real-time Ad
placements
Natural Language
Processing
Network Intrusion
Detection
Online
Recommendations
Customer Lifetime
Value

Enterprise Miner/Text Miner
• GUI interface
• Analytical data preparation
• Feature Engineering
• Sophisticated learning models
• Natural language processing
• Model life-cycle management
• Production scoring
• Integration with open source
• Industry specific modules
CAPABILITIES AND BENEFITS: TECHNOLOGY

NEURAL NETWORKS
INTRODUCTION AND INDUSTRIAL APPLICATIONS

WHAT IS A NEURAL NETWORK ?
14

WHAT IS ARTIFICIAL NEURAL NETWORK ?
15
neural network
Noun
a computer system modelled on
the human brain and nervous
system

WHAT IS ARTIFICIAL NEURAL NETWORK ?
16

WHAT ANN CAN DO ? - NEURAL NETS AS “UA” ALGORITHMS
17
Supervised learning Unsupervised learning
Semi-supervised learning

Global Sales Support & Enablement18

APPLICATIONS OF NEURAL NETWORKS
19
•Prediction of Yarn Properties in Chemical Process Technology
•Current Prediction for Shipping Guidance in IJmuiden
•Recognition of Exploitable Oil and Gas Wells
•Modelling Market Dynamics in Food-, Durables- and Financial Markets
•Prediction of Newspaper Sales
•Production Planning for Client Specific Transformers
•Qualification of Shock-Tuning for Automobiles
•Diagnosis of Spot Welds

20
•Automatic Handwriting Recognition
•Automatic Sorting of Pot Plants
•Fraud detection in credit card transactions
•Drinking Water Supply Management
•On-line Quality Modelling in Polymer Production
•Neural OCR Processing of Employment Demands
•Neural OCR Personnel Information Processing
•Neural OCR Processing of Sales Orders
•Neural OCR Processing of Social Security Forms

21
•Predicting Sales of Articles in Supermarket
•Automatic Quality Control System for Tile-making Works
•Quality Assurance by "listening"
•Optimizing Facilities for Polymerization
•Quality Assurance and Increased Efficiency in Medical Projects
•Classification of Defects in Pipelines
•Computer Assisted Prediction of Lymphnode-Metastasis in Gastric Cancer
•Alarm Identification
•Facilities for Material-Specific Sorting and Selection
•Optimized Dryer-Regulation
•Evaluating the Reaction State of Penicillin-Fermenters

MODEL ESSENTIALS: NEURAL NETWORKS
Predict new cases.
Select useful inputs.
Optimize complexity.
Prediction
formula
None
Stopped
training
...

Stopped
training
None
Predict new cases.
Select useful inputs
Optimize complexity
Prediction
formula
None
Stopped
training
...

Stopped
training
None
Predict new cases.
Prediction
formula
...

NEURAL NETWORK PREDICTION FORMULA
prediction
estimate
weight
estimate
hidden unit
bias
estimate
0
1
5-5
-1
tanh
...
activation
function
...

NEURAL NETWORK BINARY PREDICTION FORMULA
0
1
5-5
-1
tanh
0 1
5
-5
logit
link function
...

NEURAL NETWORK DIAGRAM
y
target
layer
H1
H2
H3
hidden
layer
x2
input
layer
x1
...

PREDICTION ILLUSTRATION: NEURAL NETWORKS
...
logit equation
0.0 0.50.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0
x1
0.0
0.5
0.1
0.2
0.3
0.4
0.6
0.7
0.8
0.9
1.0
x2

...
logit equation
Need weight estimates.
0.0 0.50.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0
x1
0.0
0.5
0.1
0.2
0.3
0.4
0.6
0.7
0.8
0.9
1.0
x2

...
logit equation
Log-likelihood Function
Weight estimates are found
by maximizing:
0.0 0.50.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0
x1
0.0
0.5
0.1
0.2
0.3
0.4
0.6
0.7
0.8
0.9
1.0
x2

...
logit equation 0.70
0.60
0.50
0.40
0.40
0.60
0.50
0.50
0.60
0.30
Probability estimates are
obtained by solving the logit
equation for p for each (x1, x2).^
0.0 0.50.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0
x1
0.0
0.5
0.1
0.2
0.3
0.4
0.6
0.7
0.8
0.9
1.0
x2

NEURAL NETS: BEYOND THE PREDICTION FORMULA
•
...
Interpret the modelInterpret the model.
Handle extreme or unusual values
Use non-numeric inputs
Account for nonlinearities
Manage missing values.
Handle extreme or unusual values.
Use non-numeric inputs.
Account for nonlinearities.

ORGANIC PRODUCTS
What are my potential customers ??
Can I find pointed segments for buyers??
Can I predict who will buy organic products ??
What advertising strategy I should adopt to
sell products ??
OR

PREDICTING ORGANIC PRODUCT BUYING BEHAVIOR
37
• DEMONSTRATION

UNIVERSITY & AIRLINE BOMBER – UNABOMBER
Mathematical
Genius
Youngest PhD
Holder & Professor
In jail Now !

PREDICTING AUTHORSHIP: UNSTRUCTURED DATA ML
39
• DEMONSTRATION

Prediction
formula
Best model
from sequence
Sequential
selection
Predict new cases.
Select useful inputs
Select useful inputs. None

MULTIPLE ANSWER POLL
• Which of the following are true about neural networks in SAS Enterprise Miner?
a. Neural networks are universal approximators.
b. Neural networks have no internal, automated process for selecting useful inputs.
c. Neural networks are easy to interpret and thus are very useful in highly regulated industries.
d. Neural networks cannot model nonlinear relationships.

MULTIPLE ANSWER POLL – CORRECT ANSWERS
• Which of the following are true about neural networks in SAS Enterprise Miner?
a. Neural networks are universal approximators.
b. Neural networks have no internal, automated process for selecting useful inputs.
c. Neural networks are easy to interpret and thus are very useful in highly regulated industries.
d. Neural networks cannot model nonlinear relationships.

Predict new cases.
Optimize complexity. Stopped training
Prediction
formula
Sequential
selection
...

FIT STATISTIC VERSUS OPTIMIZATION ITERATION
^logit(ρ1)logit( p ) =^
H1 = tanh(-1.5 - .03x1 - .07x2)
H2 = tanh( .79 - .17x1 - .16x2)
H3 = tanh( .57 + .05x1 +.35x2 )
logit(0.5)0
initial hidden unit weights
+ 0·H1 + 0·H2 + 0·H3
...

H1 = tanh(-1.5 - .03x1 - .07x2)
H2 = tanh( .79 - .17x1 - .16x2)
H3 = tanh( .57 + .05x1 +.35x2 )
H1 = tanh(-1.5 - .03x1 - .07x2)
H2 = tanh( .79 - .17x1 - .16x2)
H3 = tanh( .57 + .05x1 +.35x2 )
logit( p ) =^ 0 + 0·H1 + 0·H2 + 0·H3
random initial
input weights and biases
...

0 5 15 20
Iteration
10
...

0 5 15 20
validationtraining
ASE
Iteration
1 10
...

0 5 15 20
validationtraining
ASE
Iteration
6 10
...

0 5 15 20
validationtraining
ASE
Iteration
7 10
...

0 5 15 20
validationtraining
ASE
Iteration
1011
...

0 5 15 20
validationtraining
ASE
Iteration
10 13
...

0 5 20
validationtraining
ASE
Iteration
1510 18
...

0 5
validationtraining
ASE
Iteration
201510
...

ASE
Iteration
0.70
0.60
0.50
0.40
0.40
0.60
0.50
0.50
0.60
0.30
0 5 15 2010 12
...

NEURAL NETWORK TOOL REVIEW
• Create a multi-layer perceptron on selected inputs.
Control complexity with stopped training and
hidden unit count.
Thanks !!

Global Sales Support & Enablement57
NGASCE and SAS

ABOUT NMIMS AND SAS
In 1981, Shri Vile Parle Kelavani Mandal (SVKM) established Narsee Monjee Institute of Management Studies (NMIMS)
to meet the growing demand for management education.
In 2003, NMIMS was declared a deemed-to- be university under section 3 of the UGC Act 1956. With the legacy of
35+ years, NMIMS has grown to being not only one of the top 10 B-schools in India but has also emerged as a multi-
disciplinary University.
SAS tops all predictive and advanced analytics suppliers and data integration suppliers, according to the IDC report,
IDC Worldwide Business Intelligence and Analytics Tools Software Market Shares, 2016: Here Comes the Cloud. SAS
held a 30.5% market share for 2016 in the advanced and predictive analytics category, well over twice the market
share of the next-closest competitor. SAS has led in this category since IDC started tracking the market in 1997. SAS
has demonstrated continued growth every year in the category, with 2016 showing a 5 percent revenue growth.
"SAS has been able to retain authority in the advanced and predictive analytics market and continue to grow year
over year,"" said Dan Vesset, Group Vice President of Analytics and Information Management at IDC.
In addition, IDC ranked SAS as the 2016 market share leader for analytic data integration software with 21.9 percent
market share.

About NMIMS and SAS partnership
2011- 1ST SAS EAS
License procured for 1
Campus of SVKM’S
NMIMS.
2012 – Upgraded to
campus license across 4
Campus of SVKM’S
NMIMS.
2013 – SAS
Education Services
got incorporated for 3
SVKM’S NMIMS
Schools.
2014 – Upgraded to
SAS E MINER License
in 4 Campus of SVKM’S
NMIMS.
2016 | 2018 – SVKM’S Trust +
NMIMS University Upgraded
across the SVKM’S Institutions
for SAS VA License.
50+ Faculty will be SAS Trained
+ SAS Globally Certified & SAS
Accredited 2016-2017 and
another 20+ Faculties to be in
2017.
Minimum 1000 Students will be
SAS Globally certified each
year across 33 Institutions
under SVKM for the next 5
Years i.e. 2016 - 2020.
SVKM’S NMIMS Center of
Excellence – Business
Analytics & Data Sciences.
SVKM’S NMIMS DISTANCE
LEARNING ONLINE COURSES
For the first time in India, NMIMS and SAS
have joined hands to provide a platform to
working professionals and students, hard pressed for
time, to learn the most important SAS tools through
the Online Learning mode.

PROGRAM STRUCTURE
Management Program in Data Visualization
 Business Statistics [24 hrs/2 hrs per session/1 month]
 Visual Analytics [32 Hours/2 hours per session /3 months]
 Global Certification [Prep session and Certification]
 [SAS VA tool: 20 hrs during lectures and 30 hrs post lectures for practice]
* 5 months include: [Lectures, Practice sessions, Exams and Global Certification]
Executive Program in Big Data and Machine Learning (Predictive Model)
 Business Statistics [24 hours/2 hours per session/1 month]
 Analytics Bridge Course [24 hours/ 2 hrs per session/ 1 Month]
 Predictive Model-E-guide [24 hours/ 2 hours per session/2 months]
 E-Miner [24 hrs / 2 hours per session/2 months]
 Domain specific case studies “Financial risk Management”, “Marketing analytics”, “Operations and Supply chain
analytics”
* 7 months include: [Lectures, Practice sessions, Exams and Global Certification]

WHY THIS PROGRAM WITH NMIMS
 Sessions on “Business Statistics”, to strengthen your journey on Visual Analytics
 Session on “Analytics Bridge Course” benefiting your learning on Predictive Model
 Mentored sessions by SAS faculties
 Practicing data
 Fixing opportunities identified during exams
 Defined batch size for Quality interaction during lectures, 35 students per class
 Sessions conducted through Amazon Web Services, exclusively used for NMIMS
 Global Certification from SAS [Preparation session conducted by SAS]
 Weekend classes and Online interface
 Joint Certification NMIMS and SAS
 Flexible Exam pattern
 Shorter Duration
 Huge depth of Analytical content
 24/7 access to SAS Software

Questions !!
Thanks !!
For Queries
Email: ngasce@nmims.edu
Toll Free: 1-800-1025-136 Mon-Sat (10am – 6pm)

Machine Learning: Neural Networks

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