The document introduces the fundamentals of artificial intelligence and machine learning, highlighting the importance of data and algorithms in developing intelligent systems. It discusses how machine learning allows models to learn from data and make decisions, with an emphasis on supervised and unsupervised learning methods. Additionally, it outlines the impact of digital transformation and the role of various tools in implementing AI projects.

1. A N I N T R O D U C T I O N TO T H E M A G I C A L L A N D O F A RT I F I C I A L I N T E L L I G E N C E
Machine Learning For Dummies

3. T H E D I G I TA L G R O W T H C O M PA N Y

4. Projekt
Unsere Services
Digital
Products
CRM
Data
Entrepreneur
Network
Creative
Consulting Customer
Digital
Marketing

5. OUR MINDSE T
The world is changing rapidly. We are
fascinated by these upheavals. We are tackling
digital transformation - together with our
partners. We enjoy experimentation and
demand to find the best digital solution for any
given Problem!

6. 2011
Wurde die TOWA Digitalagentur gegründet.
28
Jahre ist unser Durchschnittsalter.
84
Mitarbeiter sind bei TOWA beschäftigt.

7. Philipp Freytag v. Loringhoven
D IRECTO R DATA CO NSULTING
Marketeer | Dev | Designer | Gamer | Chef

8. Artificial Intelligence
Machine Learning

9. What is Intelligence?
What defines Learning?

10. Intelligence
A mental capability, that involves the abilities:
▪ to reason,
▪ plan
▪ solve problems
▪ think abstractly
▪ comprehend complex ideas
▪ learn quickly
▪ learn from experience!

11. Learning
Learning is the transformative process of
taking in data and turning it into information
and knowledge – when internalized and
mixed with what we have experienced so far
– it changes what we „know“ and builds on
what we do.
It‘s based on input, proces and reflection.

12. Artificial Intelligence
Machine Learning

13. DEF INITIO N
It is the Science and engineering of
designing intelligent machines, especially
intelligent computer programs.

15. Miss by a faktor of 1000
2018: 33 Zettabytes 1
Until 2025: 175 Zettabytes 1
We just don’t know
1 https://de.statista.com/statistik/daten/studie/267974/umfrage/prognose-zum-weltweit-generierten-datenvolumen/

16. In Machine Learning, What is Better: More
Data or better Algorithms?

17. PE TER NORVIG , GOOGLE DIRECTO R RESE ARCH
We don’t have better algorithms. We just
have more data.

18. What he means is:
Better Data != More Data
The issue is that better data does not
mean more data. As a matter of fact,
sometimes it might mean less!

19. E XPERTS MAKE ERRORS ASWELL
The NASA lost 328 million $, because the
systems within a satellite did not use the
same units of measurement thoughout
Quelle: Wikipedia

20. E XPERTS MAKE ERRORS ASWELL
TARGET lost $5.4 billion in Canada, partly
because its inventory system was loaded
with incorrect data.
Quelle: CanadianBusiness

21. Data comes in all forms and sizes

22. A feature is an individual measurable
property or characteristic of a phenomenon
being observed

23. Feature Examples
ID Product
Catrgory
Name Color Size Sales
13 Clothing Pants Red 12 13
16 Shoes Wedges Blue 40 8
22 Shoes Ankle Boots Green 38 13
29 Accessories Necklace Yellow 3 3

24. „Big Data“
VOLME VERACIT YVARIE T Y VELOCIT Y

25. NATE SILVER , STATISTIKE R UND PUBLIZIST
Data is useless without context.

26. Machine Learning is a group of algorithms used to recognize structures in
data.
The concept assumes that it is possible to train a model (algorithm) with
data in such a way that it can make decisions.
So what does ML do?

27. Machine Learning

28. Teach (train) a model (algorithm) with
experience (data)

29. How does it work?
INPUT DATA

30. How does it work?
INPUT DATA
Model
ALGORITHM

31. How does it work?
INPUT DATA
Model
ALGORITHM OUTPUT DATA

32. Classification
supervised
▪ Labelled input
unsupervised
▪ Unlabelled input
x1
x2
x1
x2

33. Classification
supervised unsupervised
x1
x2
x1
x2
▪ Labelled input
▪ Classification and
Regression
▪ Unlabelled input
▪ Clustering and
dimension
reduction

34. Popular supervised Algorithms
▪ Nearest Neighbor
▪ Naive Bayes
▪ Decision Trees
▪ Linear Regression
▪ Support Vector Machines (SVM)
▪ Neural Networks

35. Popular unsupervised Algorithms
▪ k-means clustering
▪ Association Rules

36. What is deep learning?

37. ▪ AI > Machine Learning > Deep Learning
Machine Learning

38. Artificial Neural Networks, we‘re copying our brain!

39. Artificial Neuron
Inputs Output
„Activation“

40. Artificial Neural Networks

41. Deep? = Amount of Layers (hidden)
between Input and Output

42. ROGER KIMBAL
Welcome to The Information Age.
Data, Data Everywhere,
But Nobody Knows a Thing!

43. Business ImpactTechnology
Data
Successful
AI Project
What makes a great AI Project?

44. Applications

45. Traffic Predictions

46. Generate Haikus from news

47. Colorization of Black and White Videos

48. Adding Sounds To Silent Movies

49. Object Classification and Detection

50. Smart answers in Google Inbox

51. Game Playing

52. Translations

53. Is Machine Learning the answer to
everything?

55. Lets get to work!

56. Tools for your Choice
▪ Microsoft: https://azure.microsoft.com/de-de/services/machine-learning-studio/
▪ IBM: https://www.ibm.com/de-de/cloud/watson-studio
▪ MlJar: https://mljar.com/
▪ BigML: https://bigml.com/
▪ Datoin: https://datoin.com/

57. Microsoft Machine Learning Studio

58. Predict survival on the Titanic

59. QUESTIO N
Let's discuss, what do you think is the
most important reasons passengers
survived the Titanic sinking?

60. What we‘ll do
1. Signup to Microsoft Machine Learning Studio
2. Download the Data: https://tinyurl.com/y6qg2qh8
3. Load the data into ML Studio
4. Let‘s Explore the Data

61. Data Features
▪ pclass: A proxy for socio-economic status (SES)
1st = Upper
2nd = Middle
3rd = Lower
▪ age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5
▪ sibsp: The dataset defines family relations in this way...
Sibling = brother, sister, stepbrother, stepsister
Spouse = husband, wife (mistresses and fiancés were ignored)
▪ parch: The dataset defines family relations in this way...
Parent = mother, father
Child = daughter, son, stepdaughter, stepson
Some children travelled only with a nanny, therefore parch=0 for them.

62. What we‘ll do
1. Clean Data
▪ Drop Name,
▪ Ticket
▪ Cabin
2. Convert Text Data to Numerical Data
▪ Sex
▪ Pclass
▪ Embarked

63. Evaluation Meaning
▪ Mean Absolute Error (MAE): The mean value of the absolute errors. (An error is
the difference between the predicted value and the actual value).
▪ Root Mean Squared Error (RMSE): The square root of the average square of the
forecast errors for the test dataset.
▪ Relative Absolute Error: The mean value of the absolute errors relative to the
absolute difference between actual values and the average of all actual values.
▪ Relative Squared Error: The average of squared errors relative to the squared
difference between actual values and the average of all actual values.
▪ Coefficient of Determination: This value, also known as the R square, is a
statistical measure of how well a model fits the data.

64. In other words
Small Errors = Good!
High Coefficient of Determination = Good!
The closer Coefficient of Determination is to 1 the better!

65. Contact me in case of digital!
TO WA. THE D IGITAL GRO W TH CO MPANY
TOWA
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PHILIPP
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▪ Mail: philipp.loringhoven@towa.at