smart data webinar: machine learning (ml) adoption strategies

Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.

Machine Learning Adoption Strategies

Adrian Bowles, PhDFounder, STORM Insights, Inc.

[email protected]

mailto:[email protected]



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


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.


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 regressionLogistic regression (categorical dependent variable)

Time-series analysisClassification treesDecision trees…

Historical Data

Predicted Data

Assumptions


Psychological Processes

Perception

Learning

Motivation

Learning in Context

Memory

0. Foundation

Experience-Based

Learning1. Learn

2. Interact

3. ExpandIntegrate

Augmented/VirtualReality

Confidence-weightedReporting

Motivation

reflection

inference

Natural Cognitive Processes

deduction

Hypothesis Generation& Testing

reasoning

Natural Language Processing

Cloud

…Analytic

s

Data Management

Neu

rom

orph

icAr

chite

ctur

es Learning

Perception

A Framework for Cognitive Computing

Copyright (c) 2015-2016 by STORM Insights Inc. All Rights reserved.


Perception/NLP

Problem Solving & Learning

Simple: deterministic,

retrieve/calculate

Complex: probabalistic

hypothesize, test, rank, selectCreative:

discover, generate

OR

GA

NIZ

EDM

emor

y*

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


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


reinforcement

unsupervisedsupervised

Key approaches to MachineLearning


Key approaches to

reinforcement

MachineLearning

unsupervised

supervisedThe 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.



supervisedThe 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.


Key approaches to

reinforcement

MachineLearning

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.



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.


MachineLearning

deeplearning

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.


A New Benchmark for Deep Learning

Human

Sensors/Systems

Input Output

Representative Machine Learning Vendors


Metamind

IBM

Ersatz Labs

Scaled Inference

Microsoft

IP Soft

Numenta

Digital Reasoning

Google

Nervana Systems

BigML

Sentient Technologies

VicariousSkymind wise.io

DatoH2O

LoopAI Labs

AIBrainCycorp

NeurenceQuid

Skytree

Amazon

Cognitive Scale


Key Trend:

Open Source and ML


Key Trend:ML as a ServiceBuild With APIs

IBM Watson Services on Bluemix



(c) Amazon


Getting Started…so many choices

People

Data scientist shortage

ML skills in demand

ProductsTechnology & Vendor Selection

Process

Choose a ML strategy


Perception/NLP

Problem Solving & Learning

Simple: deterministic,

retrieve/calculate

Complex: probabalistic

hypothesize, test, rank, selectCreative:

discover, generate

OR

GA

NIZ

EDM

emor

y*

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…


What We Know

What We Want to Know


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

http://machinelearningmastery.com


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

For more information:


[email protected]

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.

Upcoming Webinar Dates & Topics

April 14 Getting Started with Streaming Analytics and the IoT

May 12 Emerging Data Management Options: Graph Databases

June 9 Advances in Natural Language Processing (NLP)

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