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MACHINE LEARNING FORBEGINNERS

–WHY IS DATA SCIENCE SO

COMPLICATED?

Matthias Heise, 29. January 2019

ALGORITHMPIGEONS

DATAPEAS

LABELSGOOD & BAD

THE FAIRY TALE OF

CINDERELLA

the algorithm

Deterministische Algorithmen vs. Machine Learning

„nach Regeln programmiert“ „gezeigt“ Regeln/Modell?

NLPNatural Language Processing

NLGNatural Language Generation

MLMachine Learning

AI Artifical Intelligence

DLDeep 

Learning

Y

LINEAR REGRESSION

X

???

Y

SUPPORT VECTOR MACHINES (SVM)

X

??

DLDeep 

Learning

the dataGarbage in – Garbage out

Y

X

Y

X

ONE‐DIMENSIONAL, NON‐LINEAR SEPARABLE

Y

X

MAPPED TO HIGHER DIMENSION, LINEAR SEPARABLE

KERNEL TRICK

Y

X

the featuresnot always obvious,

but will highly affect the algorithm

0

9

12345678

MNIST handwritten digits challenge

28 pixels

28 pixels

28 x 28 = 784 pixels at 254 grayscale… 0 55 114 254 254 10 0 0 …

… 0 0.22 0.45 1 1 0.04 0 0 …

normalized vector

corner locations + angle

X, Y, start & draw angle = 4 values

… 0 0.22 0.45 1 1 0.04 0 0 …

12 6 180 30

combine with grayscale pixels

12 6 180 30

Understanding the data

Selecting the right algorithm

Engineeringof features Model 

building(can be) solved by Deep Learning

Integrate key competences for digital transformation

Big data

Science, engineering,operations

Security

Combining traditional & new security

concepts

Cloud

One management,

any cloud

21

2008*um was founded, focus on cloud and big data

25,500employees at Orange Business Services

2,200Cloud experts

The Unbelievable Machine (*um) –now part of Orange Business Services

2018Basefarm/*um acquired by Orange Business Services

2017*um acquired by Basefarm

2,400AI and data experts

Legal Notice: Property of  Basefarm Group

Copyright © Basefarm Group 2018 All Rights Reserved. No part of this presentation may be reproduced copied,  modified or adapted, without Basefarm’s express consent.

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