This document discusses machine learning adoption strategies and provides an overview of machine learning fundamentals, the machine learning market, and getting started with machine learning. It defines machine learning and compares it to predictive analytics. It also describes common machine learning approaches like supervised, unsupervised, and reinforcement learning. The document summarizes trends in machine learning including open source tools and machine learning as a service. It concludes with tips for getting started with machine learning by assessing your data and choosing appropriate algorithms.

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Machine Learning Adoption Strategies
Adrian Bowles, PhD
Founder, STORM Insights, Inc.
info@storminsights.com

2. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Machine Learning Adoption Strategies
ML Fundamentals - What is ML, what is it good for?
Overview of the ML Market
Getting Started

3. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Machine Learning Adoption Strategies
ML Fundamentals - What is ML, what is it good for?
Overview of the ML Market
Getting Started

4. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Machine Learning vs Predictive Analytics
Machine Learning: a discipline at the intersection of computer science,
statistics, and psychology, that develops algorithms and systems capable of
improving their performance based on experience with data, rather than
predetermined rules or reprogramming.
Predictive Analytics: the use of statistical algorithms and a set of
assumptions - the model - to identify the likelihood of future outcomes or
missing values based on patterns in historical data.

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Predictive analytics: the use of statistical algorithms and a set of
assumptions - the model - to identify the likelihood of future
outcomes or missing values based on patterns in historical data.
Linear regression
Logistic regression
(categorical dependent variable)
Time-series analysis
Classification trees
Decision trees…
Historical
Data
Predicted
Data
Assumptions

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Psychological Processes
Perception
Learning
Motivation
Learning in Context
Memory

8. 0. Foundation
Experience-
Based
Learning
1. Learn
2. Interact
3. Expand
Integrate
Augmented/Virtual
Reality
Confidence-
weighted
Reporting
Motivation
reflection
inference
Natural Cognitive Processes
deduction
Hypothesis
Generation
&Testing
reasoning
Natural
Language Processing
Cloud
…
Analytics
Data Management
Neuromorphic
Architectures
Learning
Perception
A Framework for Cognitive Computing
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Perception/
NLP
Problem Solving
& Learning
Simple:
deterministic,
retrieve/calculate
Complex:
probabalistic
hypothesize, test,
rank, select
Creative:
discover, generate
ORGANIZED
Memory*
Input Class/Type
Visual
Text
Image
Aural
Speech
Music
Cues
Noise
Informative
Touch
Temperature
Tactile
Texture
Taste
Smell
Response Types
Visible (to the environment)
Verbal/NL Text
Behavioral (system changes)
Haptics/Touch/Proprioception
Invisible
Memory updates
*Corpus including data in taxonomies, ontologies, trees…
Perception

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Natural learning approaches vary. Some can be simulated with code, for
example mechanical theorem proving in formal logic.
However, a true machine learning system must improve its performance
based on experience with data, not by reprogramming.
reflectioninferencededuction
Learning
reasoning

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reinforcement
unsupervisedsupervised
Key approaches to Machine
Learning

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Key approaches to
reinforcement
Machine
Learning
unsupervised
supervised
The system is taught to detect or match patterns
based on training data. Learning by example.
The system learns/develops strategies based on
performance feedback.
An unsupervised learning system discovers patterns
based on experience.

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Key approaches to Machine
Learning
supervised
The system is taught to detect or match patterns
based on training data. Learning by example.
Good for: Applications in which there is a large body of
experience/evidence that can be codified into a training
data set with question-answer pairs.
Example: Medical diagnostics, matching symptoms to
conditions.

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Key approaches to
reinforcement
Machine
Learning
The system learns/develops strategies based on
performance feedback.
Good for: Applications in which there are too many
variables to code, but where one can recognize good/
bad behavior and reinforce/extinguish it.
Example: A guidance system for an autonomous
helicopter.

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Key approaches to Machine
Learning
unsupervised An unsupervised learning system discovers patterns
based on experience.
Good for: Applications where detecting a change in
behavior may be meaningful.
Example: Network intrusion detection.

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Machine
Learning
deep
learning
Deep learning generally refers to a biologically-inspired approach to
machine learning that leverages a collection of simple processing units -
analogous to neurosynaptic elements - that collaborate to solve complex
problems at multiple levels of abstraction.
These modern neural networks can support supervised, reinforcement, or
unsupervised learning systems.

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A New Benchmark
for Deep Learning

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Machine Learning Adoption Strategies
ML Fundamentals - What is ML, what is it good for?
Overview of the ML Market
Getting Started

19. Human
Sensors/
Systems
Input Output
Representative Machine Learning Vendors
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Metamind
IBM
Ersatz Labs
Scaled Inference
Microsoft
IP Soft
Numenta
Digital Reasoning
Google
Nervana Systems
BigML
Sentient Technologies
VicariousSkymind wise.io
Dato
H2O
LoopAI Labs
AIBrain
Cycorp
Neurence
Quid
Skytree
Amazon
Cognitive Scale

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Key Trend:
Open Source and ML

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Key Trend:
Open Source and ML

22. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Key Trend:
Open Source and ML

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Key Trend:
Open Source and ML

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Key Trend:
ML as a Service
Build With APIs
IBM Watson Services on Bluemix

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Key Trend:
ML as a Service
Build With APIs
(c) Amazon

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Key Trend:
ML as a Service
Build With APIs

27. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Machine Learning Adoption Strategies
ML Fundamentals - What is ML, what is it good for?
Overview of the ML Market
Getting Started

28. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Getting Started…so many choices
People
Data scientist shortage
ML skills in demand
Products
Technology & Vendor Selection
Process
Choose a ML strategy

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Perception/
NLP
Problem Solving
& Learning
Simple:
deterministic,
retrieve/calculate
Complex:
probabalistic
hypothesize, test,
rank, select
Creative:
discover, generate
ORGANIZED
Memory*
Input Class/Type
Visual
Text
Image
Aural
Speech
Music
Cues
Noise
Informative
Touch
Temperature
Tactile
Texture
Taste
Smell
Response Types
Visible (to the environment)
Verbal/NL Text
Behavioral (system changes)
Haptics/Touch/Proprioception
Invisible
Memory updates
*Corpus including data in taxonomies, ontologies, trees…
Getting Started…

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What We
Know
What We Want
to Know

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What We
Know
What We Want
to Know
Tip: machinelearningmastery.com is a great resource
for identifying an appropriate (set of) algorithm(s)
…
Bayesian Linear Regression
Chi-squared Automatic Interaction Detection
Classification and Regression Tree
Gaussian Naive Bayes
Least-Angle Regression
Linear Regression
Logistic Regression
Neural Network Regression
Ridge Regression
Stepwise Regression
Support Vector Machine
…
Insights?Data

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Do you have data that can be used to train the system? Examples of the types of
patterns you would like to detect? (Yes? Consider supervised learning approaches)
Are there too many variables to specify all the rules AND will you recognize good or
bad outcomes or behavior? (Yes & Yes? Look into reinforcement learning strategies)
Are you looking for novel, or previously undetected relationships or patterns? (Yes?
Consider unsupervised learning strategies)
Tips: You can mix and match learning strategies as necessary, and
tune/combine algorithms to improve performance
Getting Started…
It’s All About the Data

33. For more information:
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
adrian@storminsights.com
Twitter @ajbowles
Skype ajbowles
If you would like to connect on LinkedIn, please let me know that
you that you found me via the Smart Data webinar series.
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