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Copyright © SAS Institute Inc. All rights reserved. MACHINE LEARNING – THE WHY, WHAT, AND HOW Dr. Andreas Becks, SAS @becks_andreas
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MACHINE LEARNING –THE WHY, WHAT, AND HOWDr. Andreas Becks, SAS@becks_andreas
Copyright © SAS Inst itute Inc. A l l r ights reserved.
Maschine Learning – An Example
Everybody uses machinelearning today – in yourphoto app it identifiesfaces and persons on
your images.
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Cheap Compute Powerand Parallel Processing
“2.5 Exabytes of data are produced every day – that’s 90 years of HD videos.”
“90 % of the worlds data today has been created in the last 2 years alone.”
March 2015, DN Capital
Availability of Data R&D on Algorithms
Machine Learning – Why is ML so HOT?
Three combining trends: bigdata, massive compute
power, and better algorithms.
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Cognitive Computing
ArtificialIntelligence
Machine Learning
Neural Networks
Deep Learning
There is a lot of buzz aroundsome related terms. Oftenconfused, are they neither
separted nor the same.
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Supervised Learning
10 Algorithms Machine Learning Engineers Need to KnowTwo major groups of algorithms form a set of machine learning tools.
Source: KDnuggets based on Udacity’s Intro to Machine Learning
Unsupervised Learning
Naïve Bayes Classific.
Linear Regression
Logistic Regression
Decision Tree
Support Vector
MachinesEnsemble Methods
Unsupervised Learning
Principal Component
Analysis
Cluster Algorithms
Singular Value
Decompo-sition
Indepen-dent
Component Analysis
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The Principle: Recognizing ActivitiesLearning dependent patterns of movements
Different sports, different movements –algorithms
learns characteristic patternsin sensor data.
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Learning dependent patterns of movements
X -
Wri
st
X - Ankle
Motion Trajectories
Raw data by motionand inertial sensors
X -
Wri
st
True Activity Labels
X - Ankle
Training data: movement in context
Machine Learning
Support Vector MachinesNeural Networks
Learned classification
The Principle: Recognizing Activities
Combination of algorithms clusters the different raw data and connects it todifferent activties. Doing that, new and previously unknown incoming data can be
appropriately classified.
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Use Cases for Machine Learning
Manufacturing
• Predictive Maintenance
• Warranty reserve estimation
• Propensity to buy
• Demand forecasting
• Telematics
Healthcare
• Alerts and diagnostics from real-time patient data
• Risk stratification
• Proactive health management
Retail
• Predictive inventory planning
• Recommendation engines
• Upsell and cross-channel marketing
• Market segmentation
• ROI and customer value
Travel and Hospitality
• Aircraft scheduling
• Dynamic pricing
• Consumer feedback (social media analysis)
• Customer complaint resolution
Energy
• Smart grid management
• Power usage analytics
• Energy demand and supply optimization
• Seismic data analysis
• Carbon emission and trading
Sources: Forbes Magazine, July 2016, Harvard Business Review, February 2016
Financial Services
• Risk analytics and regulation
• Customer segmentation
• Cross-/Up-Sell
• Campaign management
• Credit worthiness evaluation
More than a third of earlymovers also saw gains in bottom-line performance using
machine-reengineering to slash 15% to 70% of costs from certain processes.
Business processes in
every industrywill be affected.
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Example: Predict Maintenance of Computer Tomographs
Thousands of devices,
Tens of thousands event codes per day,sensor data
1,000s of predictive models
Challenge: Predict failures of components 5 to 10 days in advance >70% precisionand <20% false positives
Impact on operative processes
Real applications tendto become complex.
It‘s not only 1 model / algorithm!
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Managing the Analytical Life CycleOnly integration of best models into business processes generates value
Discover Deploy
Prepare data
Explore
Model
Integrate into business processes
Execute
Evaluate
Ask
IT, Business Analyst, LoB
Robust
Automation
Actions
Decisions
Operations
Experiments
Data Science
New data
Innovation
Explorative
Data Scientist, LoB
DATA
€Analytics needs two things: building
best models AND bringing theminto production. Automation
needed due to complexity / scale.
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Summary & Next StepsCollect & explore data, learn patterns, and automate decisions.
Data Science PLUS lines of businesses
Only the integration in new processes will lead to
business value. Everyone needs to understand the
possibilities of ML.
Machine Learning improves analytics
Machine learning is an essential part
of Advanced Analytics –and every corporate
strategy.
Operationalization requiresdiscovery and action
Automation and integration are more
important than algorithms
Machine Learning
This e-book provides a primer on these innovative techniques as well as 10 best practices and a checklist for machine learning readiness.
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Get in Contact with me!
Dr. Andreas Becks, SAShttps://twitter.com/becks_andreashttps://www.linkedin.com/in/andreas-becks-10998058/
