1415 track 1 wu_using his laptop

10/24/2017
1
c o n f i d e n t i a l
twitter: @mich8elwu
linkedin.com/in/MichaelWuPhD
the black art of
machine learning
Michael Wu, PhD (@mich8elwu)
chief scientist @ lithium tech
2017.10.31
twitter: @mich8elwu
Michael Wu, PhD (@mich8elwu)
chief scientist @ lithium tech
2017.09.28
@mich8elwu
2

10/24/2017
2
twitter: @mich8elwu
• data  info  insight
 buying calcium, zinc, magnesium,
cotton balls, and switching to
unscented lotions + soaps is a
predictor of pregnancy
• decision  action
 coupons for moms, timed to specific
stages of pregnancy
• result
 ↗ revenue
$44B (2002) → $67B (2010)
THE POWER OF BIG DATA + DATA SCIENCE
btw, did you know your
daughter is pregnant?
big data + analytics
7
twitter: @mich8elwu
• data  info  insight
 filling out an loan application with only
capital or lower case letter is
predictive of loan default
• decision  action
 augment traditional underwriting
regression model w/ thousands of
variables & 10+ models
• result
 ↘ loan default rate by 40%
 ↗ market share by 25%
THE POWER OF BIG DATA + DATA SCIENCE
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10/24/2017
3
twitter: @mich8elwu
linkedin.com/in/MichaelWuPhD 9
twitter: @mich8elwu
• data has huge amount of
statistical redundancy
 duplication
 spatial + temporal correlation
 collinearity (causality)
• much info we extract from the
data are not insightful
• insights must be
 interpretable
 relevant
 novel (not already known)
DATA ≠ INFORMATION ≠ INSIGHT
big that’s
not statistically
redundant = information
data
that’s not
already known = insight
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10/24/2017
4
twitter: @mich8elwu
• relevant data: signal vs. noise
• relevance is context specific
 who:
one man’s signal is
another man’s noise
NOT ALL DATA/INFORMATION ARE RELEVANT
information
data
insight
relevant
to me
relevant to you
noise
14
twitter: @mich8elwu
• relevant data: signal vs. noise
• relevance is context specific
 who:
one man’s signal is
another man’s noise
 when?
 where?
 what’s relevant is determined by the
problem you are trying to solve or the
question you are trying to answer
NOT ALL DATA/INFORMATION ARE RELEVANT
information
data
insight
relevant
to me when I
am traveling
in Istanbul
today
noise
context is usually
specified in the
problem/question
15

10/24/2017
5
twitter: @mich8elwu
big data is
very noisy
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twitter: @mich8elwu
• how do people use data before big data?
WHY IS BIG DATA SO NOISY?
data is almost
always relevant
problem/question
Q
data collection
data
data is collected
specifically to address the
problem/question
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10/24/2017
6
twitter: @mich8elwu
• enables data capture/storage before we have a question
WHAT HAPPENS W/ BIG DATA TECHNOLOGIES?
most big data will be
irrelevant (only a tiny
% of it will be relevant)
data collection problem/question
Q
data is collected
irrespective of any
specific problem /
question / purpose
must find the “relevant
data” whenever we got
a problem/question
19
twitter: @mich8elwu
• for all data (any data):
data ≥ information ≥ insight
• for big data:
data information insight
• “a single grain of rice can tip the
scale”
• “1 bit of insightful info. may be
the difference between victory
and defeat”
DATA ≠ INFORMATION ≠ INSIGHT
information
data
insight
>> >>
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10/24/2017
7
twitter: @mich8elwu
• look beyond what’s relevant
 look at what you thought were the
irrelevant data/info
• don’t look too far beyond your
relevance boundary
 it’s costly and wasteful
 hard to establish causality
• you might not find anything, but
when you do, it will be insightful
 zest finance
WHERE DO YOU LOOK IN YOUR BIG DATA TO FIND INSIGHTS?
information
data
insight
noise
relevant
signal
21
twitter: @mich8elwu
big
data
infor-
mation
DO BIZ REALLY WANT BIG DATA?
insight business
needs
hadoop hivehbase pig
big data
tech.
noSQL impalaspark storm …
hugegap
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10/24/2017
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twitter: @mich8elwu
big
data
infor-
mation
THE BIG DATA GAP: FROM DATA TO INSIGHTS
insight
?
23
twitter: @mich8elwu
big
data
infor-
mation
FROM DATA TO INSIGHTS
insight
data scientist
is currently the only way
companies know how to fill this gap
24

10/24/2017
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twitter: @mich8elwu
so what do data
scientists do?
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twitter: @mich8elwu
DATA SCIENCE INPUT + OUTPUT
30
input output

10/24/2017
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twitter: @mich8elwu
STEP 1: GET THE DATA
data scientist is
~50% data janitor
normalization: type, range, unit,
format, foreign key ref …
exception handling: spam, missing
data, incomplete data …
dedupe, metadata tagging …
POS tagging
entity detection
sampling + sample selection
special handling for rich media
…
31
twitter: @mich8elwu
RAW DATA USUALLY DON’T PERFORM WELL
raw data
text, image, sound, video
directly measured data, etc.
“Hello, how are you?”
072 101 108 108 111 044 032 104 111 119 032 097 114 101 032 121 111 117 063
can a machine tell a bird from a plane?
how?
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10/24/2017
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twitter: @mich8elwu
raw data
35
twitter: @mich8elwu
raw data
bird (0)
plane (1)
probabilityofbird/plane
any pixel’s color/intensity
~50%
~50%
the info in a pixel is not
discriminating enough
for this task
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10/24/2017
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twitter: @mich8elwu
probabilityofbird/plane
raw data
bird (0)
plane (1)
anotherpixel’scolor/intensity
any pixel’s color/intensity
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
11
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
any pixel can be part
of a bird or a plane.
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twitter: @mich8elwu
are bad features 38

10/24/2017
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twitter: @mich8elwu
raw data are not only noisy
and “dirty,” they are bad
features!
39
twitter: @mich8elwu
FEATURES AND FEATURE ENGINEERING
raw data
features
any information you derive from the raw
data and make explicit to the learning
algorithm
namesageloan amountincome
normalized
defaultrate
~10%
~10%
any raw data
loan
income
frequency of late (or early) payment,
income (or spent) volatility (stdev),
married or not, have kids or not …
debt
income
avg. monthly spent
income
use of proper capitalization in the application
# saves before submitting,
avg. time between saving (or opening) the application,
date, time, day of week when filling the application …
hair color, eye color,
height, weight …
where did they fill out the application,
sunny or rainy when filling the application …
online application
x = name, age, ID
info, loan amount,
income, spending
+ payment habit…
any
any feature
normalized
defaultrate
the info doesn’t even have
to be in the raw data, they
just have to be derivable
height
feature engineering
the extraction of implicit (or externally
derived) information in (from) the raw data
feature
engineering
feature engineering
the extraction of implicit (or externally
derived) information in (from) the raw data
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10/24/2017
14
twitter: @mich8elwu
occurrenceprobabilityofbeak
occurrence probability of stabilizer
COMING UP WITH BETTER FEATURES
raw data
birds:
have beak,
have eyes,
have feets,
have feathers …
plane:
have stabilizers,
have engines,
have windows …
feature
engineering
model
obtained by optimizing some
objective function (error, likelihood,
etc.) + model validation
statistics
features
algorithm
41
twitter: @mich8elwu
occurrenceprobabilityofbeak
occurrence probability of stabilizer
COMING UP WITH BETTER FEATURES
raw data
birds:
have beak,
have eyes,
have feets,
have feathers …
plane:
have stabilizers,
have engines,
have windows …
model
features
algorithm
42
machine learningfeature
engineering statistics

10/24/2017
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twitter: @mich8elwu
data science is
~25% handcrafting
… of features
44
twitter: @mich8elwu
“Coming up with features is difficult,
time-consuming, requires expert
knowledge. Applied machine learning
is basically feature engineering.”
—Andrew Ng
hand crafted features are:
- domain specific,
- task specific,
- not generalizable
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10/24/2017
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twitter: @mich8elwu
CAN WE LEARN “GOOD FEATURES” DIRECTLY FROM THE DATA?
47
twitter: @mich8elwu
raw data
birds:
have beak,
have eyes,
have bird feet,
have feathers …
plane:
have stabilizers,
have engines,
have windows …
feature
engineering
model
statistics
features
algorithm
48

10/24/2017
17
twitter: @mich8elwu
raw data
birds:
have beak,
have eyes,
have bird feet,
have feathers …
plane:
have stabilizers,
have engines,
have windows …
feature
engineering
model
statistics
features
algorithm
shapes edges pixels
49
twitter: @mich8elwu
traditional
machine learning
handcrafted
by experts
work for most (80%) of the
problems in business
faces
DEEP LEARNING
raw data
feature
engineering
model
statistics
features
algorithm
deep learning
deep neural network
automatically learned from the data
with different levels of abstraction
....
input=
layer3
layer2
layer1
carselephantschairsfaces
+cars
+airplanes,
+motorbikes
combination of
pixels → edges
combination of edges
→ object parts combination of parts → the object
50

10/24/2017
18
twitter: @mich8elwu
traditional
machine learning
handcrafted
by experts
work for most (80%) of the
problems in business
DEEP LEARNING
raw data
feature
engineering
model
statistics
features
algorithm
deep learning
deep neural network
automatically learned from the data
with different levels of abstraction
google brain:
16,000 cpu
1,000,000,000+
connections
10,000,000
training images
from youtube
extraordinarily generalizable:
makes machine behaves & think more like
human, but requires lots of data to train
success stories:
computer vision: image labeling, search …
audio signal processing: speaker ID, speech
recognition (speech-text) …
text processing: machine translation, etc.
interesting problems in the industry:
—sentiment analysis
—actionability & intention prediction
—fraud, spam detection …
51
twitter: @mich8elwu
domain
expertise
WHAT DO DATA SCIENTIST DO?
raw data
feature
engineering
model
statistics
features
algorithm
computer
science
math +
statistics
communication
data visualization, storytelling,
translation of data to business
insights, decisions, and action
domain
expertise
plumbing
cleaning
janitoring
handcrafting
52

10/24/2017
19
twitter: @mich8elwu
domain
expertise
WHAT DO DATA SCIENTIST DO?
raw data
feature
engineering
model
statistics
features
algorithm
computer
science
math +
statistics
communication
data visualization, storytelling,
translation of data to business
insights, decisions, and action
domain
expertise
domain
expertise
math +
statistics
computer
science
data
science
plumbing
cleaning
janitoring
handcrafting
54
twitter: @mich8elwu
thank you, q&a,
+ follow me
twitter: @mich8elwu
135

10/24/2017
20
twitter: @mich8elwu
want to dig deeper?
sos sos2
http://pages.lithium.com/science-of-social
http://www.lithium.com/library/science-of-social-2
136

1415 track 1 wu_using his laptop

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1415 track 1 wu_using his laptop