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C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MACHINE LEARNING AND SAS
KAARE BRANDT PETERSEN, 13. OKT 2016
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MACHINE LEARNING MACHINE LEARNING IN THE MEDIA
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MACHINE LEARNING EXAMPLE FROM THE RESEARCH FRONT LINE
Source: https://research.googleblog.com/2014/11/a-picture-is-worth-thousand-coherent.html
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MACHINE LEARNING WHAT IS MACHINE LEARNING?
[Machine learning is the] field of study that gives computers the ability to learn without being explicitly programmed
Arthur Samuel (1901-1990), USA
Pioneer in computer games
First self-learning program playing checkers, 1959
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MACHINE LEARNING THE ROLE OF THEORY – AND WHEN THERE IS NO THEORY
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MACHINE LEARNING WHAT IS MACHINE LEARNING (ANOTHER DEFINITION)
Advanced models Related concepts Ways to deal with data
Neural networks
K-Nearest-Neighbours
Support Vector Machines
Random Forrests
…
Complexity
Overfitting
Regularization
Bias-variance trade-off
Ensemble learning
…
Data partitioning
Cross-validation
Leave-one-out
Bagging
Boosting
…
+ +
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MACHINE LEARNING HOW DOES IT LOOK IN SAS EM?
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MACHINE LEARNING LETS GET REAL – SOME COMMON MISUNDERSTANDINGS
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MACHINE LEARNING
40 x 40 pixels
3 colors
= 4800 input dim
1024 x 768 pixels
3 colors
= 2.36 mio input dim
Text
Images
Sound
Sensors
HF Times Series
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
DEEP LEARNING THE CAT PROBLEM
Extracting image features of a cat – but cats have many
formsBrutto list of 1.000.000.0000 images
Amazon Mechanical Turk:
* 48940 persons categorizing and sort
* 15.000.000 img in 22.000 categories
* 62.000 images of cats
Convoluted neural networks (Hinton et al.)
24 millions nodes
140 millions parametes
15.000 million connections
Source: Fei Fei Li, Director of Stanford AI & Vision Lab, TED Talk 2015
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
DEEP LEARNING WHAT IS DEEP LEARNING?
[Deep] learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification
Geoffrey Hinton (1947-*),
Godfather of Deep Learning
Born in England, Lives in Canada
University of Toronto
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
DEEP LEARNING DEEP LEARNING OVER-SIMPLIFIED INTO ONE SLIDE
Input and output must match (as best possible). Then the middle
layer act as a compressed representaiton of the full image
= ”Cat”
2
1 Unsupervised
part for finding
the optimal
representation
Supervised
learning on the
optimal
representation
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
SAS VIYAMACHINE LEARNING IN SAS VIYA
… (AND DEEP LEARNING COMING UP TOO)
Source: http://video.sas.com/detail/videos/#category/videos/sas-viya-data-mining-and-machine-learning
C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
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Machine Learning with SAS
• Introduction to ML – concepts, motivation, possibilities
• Learn how to in SAS EM
• December 12-13 (2 days)
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