This document discusses machine learning and topological data analysis using Ayasdi software. It provides an introduction to Ayasdi and topological data analysis, and explains how Ayasdi uses machine learning techniques like neural networks and random forests along with topological data analysis to better understand, segment, and act on data. Specifically, Ayasdi provides clearer segmentation of data and better identification of errors compared to traditional machine learning approaches. The goal is to help people leverage their data more effectively.

1. Machine Learning with Ayasdi
2014

2. Agenda
PRESENTERS
Introduction
TJ Laher
Product Marketing
Ayasdi Introduction
@Tjlaher
The State of Data Analysis Today
Alexis Johnson
Sr. Solution Analyst
Machine Learning and
Topological Data Analysis
@Ayasdi
JOIN THE DISCUSSION
#AyasdiWebinar
CONFIDENTIAL
2

3. Topological Data Analysis (TDA)
1735
Famous math
problem, 7
Bridges of
Königsberg
“
1900’
s
Topology grows
in popularity in
the math
community
2000
DARPA funds
$10M for
topology (TDA)
research
2008
Ayasdi founded to
commercialize
DARPA research
into a product
2013+
TDA used by some
of the world’s
largest companies
Ayasdi’s approach is using Topological Data Analysis one of the
top 10 innovations developed at DARPA in the last decade.
”
Tony Tether, Director
Defense Advanced Research Projects Agency (2001-2009)
3

4. Ayasdi Solutions, Impacting Every Business
Discover Drugs
Optimize Energy
Production
Identification of
Emerging
Threats
CONFIDENTIAL
Prevent Millions
in Fraud Losses
4

5. Traditional Data Analysis
SELECT *
FROM
TABLE
Hypothesis
5

6. Automated Discovery
Answers
First
6

7. Operationalizing Ayasdi Applications
Ayasdi
Applications
(Built on Ayasdi Core)
Dashboards | Alerts | Classification | Prediction | Discovery | Custom Outputs
Topological Data Analysis (TDA)
& Machine Learning
Ayasdi Core
Technology
Compute Infrastructure
APIs
Classification
and
Prediction
Inputs
77

8. Why Do We Need Machine Learning?
Understand Data
Supervised
Unsupervised
Act On Data
Segmentation-
Regression–
Patients
Prospects
Transactions
Drug Response
Oil Production
Market Trends
Discovery –
Classification–
New Fraud
Biomarkers
Atypical Behavior
Fraud Waste & Abuse
Predictive Maintenance
Anomaly Detection
88

9. Machine Learning Techniques
Understand Data
Supervised
Machine Learning Techniques:
Neural Networks
Random Forest
…
TDA Supervised
Unsupervised
Unsupervised
Act On Data
Machine Learning Techniques:
Principle Component Analysis
Multidimensional Scaling
…
TDA Unsupervised
9

10. Common Challenges with Machine Learning
Systematic Error
Hypothesis Driven
Data
Algorithm
Output


o
o

VS.
Too Many Questions to Ask
T
T
F
F
T
Identification of Error
Algorithmic Reach
Information Loss
Unfamiliar with New Techniques
Dimensionality Reduction
100
1

11. Examining Unsupervised Analysis
Traditional Unsupervised Output
Unsupervised TDA Output
Unclear Segmentation
Clear Segmentation
Signals Confusion
Eliminated Irrelevant Signals
11

12. Examining Supervised Analysis
Tradition Supervised Output
Supervised TDA Output
Error
Correction of Error
Inability to Identify Error Cause
Clear Identification of Model Failure
Opaque Understanding of Prediction
Identify How Random Forest is
Predicting
12

13. Machine Learning with Ayasdi
Machine Learning
The Ayasdi Difference
Coders
Trained Individuals
Handful of Algorithms
100s of Algorithms Available
Noisy Segmentation
Clear Segmentation
The goal of all these methods is enabling people to leverage their data more
effectively. Together, Ayasdi and machine learning make this possible.
13

14. Data has shape
Q&A
Shape has meaning
Meaning drives value
CONTACT US
Alexis Johnson
Sr. Solution Analyst
@Ayasdi
www.Ayasdi.com
sales@ayasdi.com
TJ Laher
Product Marketing
@Tjlaher