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