1. Machine Learning Oracle Hrvatska Igor Rajić
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Oracle Hrvatska
Igor Rajić
Business Development Manager Fusion Middleware & Business Analytics

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3. Traži se „Data Scientist”!
Harvard Business Review „Data Scientist: The Sexiest Job of the 21st Century”
Glassdoor: The best job in USA
Deloitte, IDC, Gartner itd..
Machine Learning ?

4. Advanced analytics
Predictive analytics
Data Mining
Machine learning

5. Machine Learning
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.
Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

6. Machine Learning in commercial sector
Targeting the right customer with the right offer
How is a customer likely to respond to an offer?
Finding the most profitable growth opportunities
Finding and preventing customer churn
Maximizing cross-business impact
Security and suspicious activity detection
Understanding sentiments in customer conversations
Reducing medical errors & improving quality of health
Understanding influencers in social networks
Spam filtering
Typical Data Mining and Predictive analytics use cases:
Targeting the right customer with the right offer
How is a customer likely to respond to an offer?
Finding the most profitable growth opportunities
Finding and preventing customer churn
Maximizing cross-business impact
Security and suspicious activity detection
Understanding sentiments in customer conversations
Reducing medical errors & improving quality of health
Understanding influencers in social networks

7. ML in Education
Content analytics that organize and optimize content modules
Learning analytics that track student knowledge and recommend next steps:
Adaptive learning systems
Game-based learning
Dynamic scheduling matches students that need help with teachers that have time
Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading
Confidential – Oracle Internal/Restricted/Highly Restricted

8. Preparing the data
Preparing the data
„Learning”
Model selection
Evaluation
Deployment
Prediction

9. Predicting house prices – model 1 linear

10. Predicting house prices – model 2 quadratic

11. Predicting house prices – model 3 high order polynomial

12. Predicting house prices - overfitting

13. Multiple features (input variables)
Other possible features
# bedrooms
Plot size
Quality of neighborhood
Year of construction …
Few 100s typical

14. Where does complexity come from?
Model choice
Number of „features” (input variables) – F
Text – every word is a feature
Genetics – features
Number of records of data - R
In general it is important that R>>>F
Big Data
High volume of data helps with avoiding overfitting via cross validation and other techniques

15. More Data Variety—Better Predictive Models
Increasing sources of relevant data can boost model accuracy
100%
100%
Model with “Big Data” and hundreds -- thousands of input variables including:
Demographic data
Purchase POS transactional data
“Unstructured data”, text & comments
Spatial location data
Long term vs. recent historical behavior
Web visits
Sensor data
etc.
Model with 75 variables
Model with 250 variables
Model with 20 variables
Naïve Guess or Random
True positive Rate
False Positive Rate 0%

16. Other models Identify most important factor (Attribute Importance)
A1 A2 A3 A4 A5 A6 A7
Identify most important factor (Attribute Importance)
Predict customer behavior (Classification)
Predict or estimate a value (Regression)
Find profiles of targeted people or items (Decision Trees)
Segment a population (Clustering)
Find fraudulent or “rare events” (Anomaly Detection)
Determine co-occurring items in a “baskets” (Associations)

17. Unsupervised machine learning - clustering
Find similar!

18. As a conclusion (so far)
Machine Learning in general is a complex subject area which requires highly skilled people – data scientists
combination of IT and mathematical/statistical skills
Majority of technologies and implementations are batch (slow) or recently near real-time, specially related to the „learning” process of ML
Can we simplify it?
Can we do it in real-time?

19. Data Miner Survey 2016 by Rexer Analytics
While 6 out 10 data miners report the data is available for analysis within days of capture, the time to deploy the models takes substantially longer. For 60% of the respondents the deployment time will range between 3 weeks and 1year.
Everyone forgets about deployment – but is most important component!

20. Traditional vs. Oracle Machine Learning/Predictive Analtyics
Traditional— “Move the data”
—“Don’t move the data!”
Oracle Confidential – Internal/Restricted/Highly Restricted

21. Traditional vs. Oracle Machine Learning/Predictive Analtyics
Traditional— “Move the data”
— “Move the algorithms”
Simpler, Smarter Data Management + Analytics / Machine Learning Architecture
Oracle Confidential – Internal/Restricted/Highly Restricted

22. Zagrebačka Bank (biggest bank in Croatia)
Increases Cash Loans by 15% Within 18 Months of Deployment
Objectives
“With Oracle Advanced Analytics we execute computations on thousands of attributes in parallel—impossible with open-source R. Analyzing in Oracle Database without moving data increases our agility. Oracle Advanced Analytics enables us to make quality decisions on time, increasing our cash loans business 15%.”
– Jadranka Novoselovic, Head of BI Dev., Zagrebačka Bank
Needed to speed up entire advanced analytics process; data prep was taking 3 days; model building 24 hours
Faster time to “actionable analytics” for Credit Risk Modeling and Targeted Customer Campaigns
Solution
“We chose Oracle because our entire data modeling process runs on the same machine with the highest performance and level of integration. With Oracle Database we simply switched on the Oracle Advanced Analytics option and needed no new tools,”
– Sinisa Behin, ICT coordinator at BI Dev. Zagrebačka Bank
Zaba migrated from SAS to the Oracle Advanced Analytics platform for statistical modeling and predictive analytics
Increased prediction performance by leveraging the security, reliability, performance, and scalability of Oracle Database and Oracle Advanced Analytics for predictive analytics—running data preparation, transformation, model building, and model scoring within the database
ZabaBank Oracle Customer Snapshot on OTN

23. Oracle RTD Self Learning
RTD Complements Traditional Data Mining
Traditional Learning Process: models lag by weeks or months
Source Databases
Analytical Mart
Data Mining Tools
Scores and Lists
Operational Applications
feedback: days or weeks
Continuous Self-Learning Process: models are updated in real-time
Advantages:
Automatic model creation
Quick to react when behavior changes
Both learning and scoring in Real-Time
Allows broader scope of analysis
Simple to implement and run
Self-Learning Analytics
Operational Applications
input from external models and lists
events
decisions
feedback: immediate

24. To a more dynamic approach
Real-time & fully automated
Define some basic marketing rules
Manual Activity
Automated
RTD self-learns based on success, all relevant data and rules. Auto adjusts future offers accordingly
Supply all relevant data
RTD
RTD informed on success or failure of each offer
Interact with the Individual Customer (e.g. Website, App, , Call, or Mail)

25. Machine Learning in financial industry:
Oracle Cloud Day May 12th 2016
Machine Learning in financial industry:
Oracle RTD implementation in PBZ
Maja Salamon
Project manager
Privredna banka Zagreb
Zagreb, 12 May 2016

26. Business have a silo view of channels
Buying Journey Has Become More Complex
Store
Web
Mobile
Social
Call Center
Pricing
Promotions
Order Capture
Logic
Data
Tablet
Siloed Channels Create Inconsistency
Store
Call Center
Web
Mobile
Tablet
Social
Research
Shop
Buy
Pickup
Service
Browse
Reviews
Search
Receive Offer
Check With Friends
Product-Related Call
Inspect Product
Chat
Online
Write Review
In-Store
Tweet
Like
Check Delivery
Status
Call for Accessory Information
Follow-On
Purchase

27. Betfair boosts revenue with Oracle analytics
James Knight, web capabilities product manager at Betfair, told delegates yesterday at Gartner's Business Intelligence Summit that an initial 23 potential suppliers was eventually cut down to one.
"We went through a rigorous selection process and found 23 potential suppliers. We cut this to a shortlist of six, who performed technology presentations for us, after which we cut to three suppliers."
After visiting firms around the US and Europe, Betfair eventually decided to select Oracle's RTD.
"We've seen a 400 per cent uplift in click rate in the target group which is driven by RTD," explained Knight.

29. Oracle’s Advanced Analytics
Fastest Way to Deliver Scalable Enterprise-wide Predictive Analytics
avings
Model “Scoring”
Embedded Data Prep
Data Preparation
Model Building
Oracle Advanced Analytics
Secs, Mins or Hours
Traditional Analytics
Hours, Days or Weeks
Data Extraction
Data Prep &
Transformation
Data Mining
Data Prep. &
Data Import
Major Benefits
Data remains in Database & Hadoop
Model building and scoring occur in-database
Use R packages with data-parallel invocations
Leverage investment in Oracle IT
Eliminate data duplication
Eliminate separate analytical servers
Deliver enterprise-wide applications
GUI for Predictive Analytics & code gen
R interface leverages database as HPC engine

30. “We increased our revenue by 40%”
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Mark Sucrese, Marketing IT Director, Dell
Oracle Confidential – Internal/Restricted/Highly Restricted

31. Customer & Brand 360
Powered by Oracle Real-Time Decisions, BI, Siebel, Eloqua
Improve revenues and operations through personalized predictive analytics
$132M in net new revenue FY2012
40% reduction in cost of dispatch
“The insights are amazing. You can really see customers' buying patterns and interests, how they change over time, and we can take action on that.”
Mark Sucrese, Marketing Director
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Oracle Confidential – Internal/Restricted/Highly Restricted

32. Dell RTD is Live in 3 Channels, 4th is WIP
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Service Contact Centre
Sales Contact Centre
content personalization
Oracle Confidential – Internal/Restricted/Highly Restricted

33. Project#3: Email Personalization
Personalize subjects header to increase opening rates
Send s at optimal time based on learned behaviors.
Propose Upsell / Cross-Sell offers at mail opening with high probability of a sale transformation
Learn in real-time on mail opening, click, cart, revenue generating positive events
Oracle Confidential – Internal/Restricted/Highly Restricted

34. Oracle’s Unified Big Data Management and Analytics Strategy
Existing Sources
Oracle Big Data SQL
Tables in Hadoop
Tables in DB
SQL join
In-Memory Appliance
Oracle BI Foundation Suite & Data V.
Productize, Secure & Govern
Data Warehouse
Oracle Database
Oracle Advanced Analytics
Experiment, Prototype, Collaborate
Quickly find, explore, transform, discover and share in BDD
Publish results to HDFS
Use to build predictive models with Oracle R for Hadoop
Exadata
Exalytics
BDA
Oracle Real-Time Decisions
Oracle Big Data Discovery is one component in Oracle’s overall big data management and analytics strategy. Many of our customers that want to build an entire big data architecture based on a set of unified and integrated big data technologies from a single vendor.
[build]
Customers can leverage Oracle Big Data Discovery and Oracle R for Hadoop together to experiment, prototype and collaborate on new data sets in the big data lab.
As new discoveries are uncovered and published in Hadoop, they can then be secured and governed through the data warehouse for access by thousands of users across the enterprise.
Customers can choose to quickly copy data between hadoop and the data warehouse at high speed
or leverage a product like Oracle Big Data SQL to query the data in HDFS without moving it at all.
Big data SQL allows any application built on Oracle SQL to seamlessly leverage data in Hadoop with no changes
so tools like Oracle BI Foundation Suite can benefit from all the hard work performed in the big data lab.
Finally, all of these architecture components can be purchased as engineered systems allowing customers quickly deploy applications and gain all the performance benefits and lower total cost of ownership from hardware and software designed to work together.
Productize, Secure, Govern
Connect published HDFS files to secure Oracle DB using Oracle Big Data SQL
No data movement required
Seamlessly extends existing DWH and BI investments with non- traditional data in Hadoop
Experiment, Prototype & Collaborate
Data Reservoir
Oracle Big Data Discovery
Hadoop (HDFS)
ORAAH
Emerging Sources
Available as Engineered Systems

35. The Result of Silo’d Marketing is A Broken Customer Experience1
The Result of Silo’d Marketing is A Broken Customer Experience
Marketers Lean Heavily
on Fragmented Tools
Pass Fragmentation
Onto Customer
Bombarded, Customers Don’t Convert or They Leave
78% of customers don’t receive consistent experience across channels — Accenture
94% of customers have discontinued communication with a company because of irrelevant messages — Blue Research

36. Real-Time Interactions Optimization Value Dimension
Option 3:
Real-Time Self-learning + Offline Data Mining + Rules + Performance Goals
Option 2:
Offline Data Mining + Rules
Effectiveness
Option 1:
Rules only
Doing Nothing
Efficiency
TCO
Oracle Confidential

37. Oracle Confidential – Internal/Restricted/Highly Restricted

38. Many Organizations Are Facing Similar Challenges
Objectives
Problems Today
Over-reliance on business rules
Long lead times between analysis and deployment
Poor channel integration
Siloed solution for each channel
Poor real-time performance and scalability
Best Practice
Balance between model-based and user-defined decisions
High degree of automation / Self-learning models
Pervasive solution spanning all customer-facing applications
Common set of models and metadata for all channels
Service-oriented architecture with guaranteed response times
Timeliness and Relevance
Multi-Channel Support
Ako se niste prepoznali potencijalne primjene u vašoj organizaciji evo još par ideaj.
Poslovna pravila.
Ease of Integration
Source: Oracle Insight analysis

39. Classification Targeting the right customer with the right offer
How is a customer likely to respond to an offer?
Finding and preventing customer churn
Security and suspicious activity detection
Understanding sentiments in customer conversations
Reducing medical errors & improving quality of health
Spam filtering

40. Classification - Logistic regression
Uses Generalized Linear Model for scoring
Logit function
# day negative account balance
# day positive account balance

41. Classification - Logistic regression
Score
sigmoid(Score)

42. Oracle’s Advanced Analytics (Machine Learning Platform)
Multiple interfaces across platforms — SQL, R, GUI, Dashboards, Apps
Information Producers
Information Consumers
Users
R programmers
Data & Business Analysts
Business Analysts/Mgrs
Domain End Users
R Client
SQL Developer/ Oracle Data Miner
OBIEE
Applications
Platform
Hadoop
Oracle Database Enterprise Edition
ORAAH
Parallel, distributed algorithms
Oracle Advanced Analytics - Database Option
SQL Data Mining, ML & Analytic Functions + R Integration for Scalable, Distributed, Parallel in-DB ML Execution
HQL
Oracle Database 12c
Oracle Cloud
Advanced Analytics

43. You Can Think of Oracle’s Advanced Analytics Like This…
Traditional SQL
Oracle Advanced Analytics - SQL &
“Human-driven” queries
Domain expertise
Any “rules” must be defined and managed
SQL Queries
SELECT
DISTINCT
AGGREGATE
WHERE
AND OR
GROUP BY
ORDER BY
RANK
Automated knowledge discovery, model building and deployment
Domain expertise to assemble the “right” data to mine/analyze
Analytical SQL “Verbs”
PREDICT
DETECT
CLUSTER
CLASSIFY
REGRESS
PROFILE
IDENTIFY FACTORS
ASSOCIATE +

44. R—Widely Popular R is a statistics language
R environment
Strengths
Powerful & Extensible
Graphical & Extensive statistics
Free—open source
Challenges
Memory constrained
Single threaded
Outer loop—slows down process
Not industrial strength

45. Oracle Advanced Analytics Database Evolution Analytical SQL in the Database
New algorithms (EM, PCA, SVD)
Predictive Queries
SQLDEV/Oracle Data Miner 4.0 SQL script generation and SQL Query node (R integration)
OAA/ORE adds NN, Stepwise, scalable R algorithms
Oracle Adv. Analytics for Hadoop Connector launched with scalable BDA algorithms
ODM 11g & 11gR2 adds AutoDataPrep (ADP), text mining, perf. improvements
SQLDEV/Oracle Data Miner 3.2 “work flow” GUI launched
Integration with “R” and introduction/addition of Oracle R Enterprise
Product renamed “Oracle Advanced Analytics (ODM + ORE)
Over the years, Oracle has increasingly invested in making the database and SQL more powerful….
Oracle Data Mining 10gR2 SQL - 7 new SQL dm algorithms and new Oracle Data Miner “Classic” wizards driven GUI
SQL statistical functions introduced
Oracle Data Mining 9.2i launched – 2 algorithms (NB and AR) via Java API
Oracle acquires Thinking Machine Corp’s dev. team + “Darwin” data mining software
7 Data Mining “Partners”
1998
1999
2002
2004
2005
2008
2011
2014

46. Agenda
Prema mnogim studijama "Data Scientist" je jedan od onih poslova budućnosti za koje već sada nedostaje veliki broj obrazovanih stručnjaka.
Djelatnost "Data Scientista" je usko vezana uz područje Machine Learning-a (ML)
Što je ML i gdje se sve koristi?
Koje poslove obavljaju "Data Scientisti"?
Kako možemo primijeniti ML u obrazovanju?
Primjere implementacija ML u Hrvatskoj u financijskom sektoru