nils svangÅrd data science & machine learning · 2014-04-03 · – eric schmidt, ceo google,...
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D ATA S C I E N C E & M A C H I N E L E A R N I N G
N I L S S VA N G Å R D
– A N G E L A A H R E N D T S , C E O O F B U R B E R R Y
“Consumer data will be the biggest differentiator in the next few years. Whoever unlocks the reams of data and uses it strategically will
win.”
– E R I C S C H M I D T, C E O G O O G L E , 2 0 1 0
“Every 2 Days We Create As Much Information As We Did Up To 2003”
2010 2013 2015
2 0 1 3 S K A PA D E S S A M M A M Ä N G D VA R 1 0 : E M I N U T
K O N K U R R E N S F Ö R D E L A RB I G D A TA L E D E R T I L L
Grocers
Online Retailers
Big Box Retailers
Casinos
Credit Cards
Insurance 8%
9%
5%
5%
-1%
6%
9%
14%
11%
9%
24%
12%
Big Data Leaders
Other
M C K I N S E Y G L O B A L I N S T I T U T E , B I G D ATA : T H E N E X T F R O N T I E R F O R I N N O VAT I O N , C O M P E T I T I O N , A N D P R O D U C T I V I T Y, M C K I N S E Y & C O M PA N Y, 2 0 1 1 .
B I G D ATA B U S I N E S S I N T E L L I G E N C E V S .
The IBM 702: a computer used by the first generation of AI researchers.
• Mer Beräkningskraft
• Mer Data
• Bättre Teknik
G A R T N E R , H T T P : / / W W W. G A R T N E R . C O M / I T / PA G E . J S P ? I D = 1 8 6 2 7 1 4
“Through 2015, more than 85% of Fortune 500 organizations will fail to effectively
exploit big data for competitive advantage.”
Big Data
Big Data
Data Science
M C K I N S E Y / W S J , H T T P : / / O N L I N E . W S J . C O M / A R T I C L E /S B 1 0 0 0 1 4 2 4 0 5 2 7 0 2 3 0 4 7 2 3 3 0 4 5 7 7 3 6 5 7 0 0 3 6 8 0 7 3 6 7 4 . H T M L
"A significant constraint on realizing value from Big Data will be a shortage of talent, particularly of people with
deep expertise in statistics and machine learning," ... "We project a need for 1.5 million additional [data scientists] in
the United States who can [use] Big Data effectively."
M A C H I N E L E A R N I N GD E N M E S T P O P U L Ä R A K U R S E N PÅ S TA N F O R D 2 0 1 3
M A C H I N E L E A R N I N G
A R T I F I C I A L I N T E L L I G E N C E V S .
E X E M P E L
• OCR
• Spamfilter
• Sökmotorer
• Face Recognition
E - H A N D E L
P O L I T I K
M A C H I N E L E A R N I N G A L G O R I T H M
T R A I N I N G D ATA
T E S T D ATA M O D E L P E R F O R M A N C E
F E E D B A C K
B A T C H
R E A LT I M E
M A C H I N E L E A R N I N G D O E S N O T R E Q U I R E B I G D ATA
I V E R K L I G H E T E N
9 9 . 6 5 %
9 9 . 9 8 4 1 6 %4 0 0 T R U E P O S I T I V E
2 9 , 6 0 0 FA L S E P O S I T I V E
8 0 / 2 0
S M A K A R D E T S Å K O S TA R D E T
Demand SideSupply Side
Publishers
Advertisers
Ad Market
Exchanges Agencies
B R A R E S U LTAT M E D E N K L A M E D E L
T H I N K O U T S I D E T H E B O X
Define Indicators
Tune Indicator Scores
Quantitative Analysis of Indicators
Current Player Data
Train Predictive
Model
All Historical Player Data
Continuously Predict Risks
for Active Players
Latest Player Data
2-4 times per yearDuring initial dev phase,
and then once every year
"Psychosocial modelling" Predictive Modelling
Operator:Communication & User InterfaceWeekly
F R A M T I D E N ?
M A C H I N E 2 M A C H I N E
I N T E R N E T O F T H I N G S
D E E P L E A R N I N G
S A M M A N FAT T N I N G
• Data Science är här för att stanna
• Det går att få bra resultat med enkla medel
• Experimentera mera!
H T T P S : / / C L A S S . C O U R S E R A . O R G / M L - 0 0 3 / L E C T U R E
VA R K A N M A N L Ä R A S I G M E R ?
TA C K !
N I L S S VA N G Å R D N I L S @ TA J I T S U . C O M 0 7 0 2 - 8 6 3 7 6 3
TAJITSUM A X I M I Z I N G C U S T O M E R VA L U E
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