Machine Learning 101, as presented at SAPTechEd Barcelona, nov 2016

1. ANP126
Machine Learning:
Hype or Hit?
Fred Verheul

2. Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
1. Demo, SAP ICN
2. Skill set for aspiring ML experts
3. Take-aways
2

3. Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
1. Demo, SAP ICN
2. Skill set for aspiring ML experts
3. Take-aways
3

4. Machine Learning
"Field of study that gives computers the ability to learn
without being explicitly programmed” (Arthur Samuel, 1959)
4

5. What is Machine Learning?
5
Computer
Computer
Traditional Programming
Machine Learning
Data
Data
Program
Output
Program
Output

6. Examples: Recommender systems
6

7. Examples: Natural Language Processing
7
Siri
Google Translate

8. Examples, continued…
8
SPAM-
filtering
Handwriting
recognition

9. ML in the news: IBM Watson
9

10. ML in the news: Deepmind’s AlphaGo
10

11. ML in the news: business example
11

12. Vendor Platforms…
12

13. Tricking a neural network…
13
A cat! Surely also a cat?!
More examples and explanation by Julia Evans (@b0rk)

14. Machine Learning gone wrong
14

15. Data Mining Fail (by Carina C. Zona)
15

16. Prediction is hard…
16

17. Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
1. Demo, SAP ICN
2. Skill set for aspiring ML experts
3. Take-aways
17

18. CRISP-DM: data mining process
18
ML
important
ML
important

19. Data: terminology
19
feature
target /
label
instance

20. Examples of ML tasks
Supervised learning
Regression 
target is numeric
Classification 
target is categorical
20
Unsupervised learning
Clustering
Dimensionality
reduction

21. Exploratory Data Analysis
21

22. Data preparation
• Data Cleaning
• Missing Data
• Feature Engineering
• Normalization
• Categorical data  Numerical features
• Log-based features or target
• Date/time-related features
• Combine features, e.g. by +, -, x, /
22

23. Modeling: so many algorithms…
23

24. ML Algorithms: by Representation
Collection of candidate models/programs, aka hypothesis space
24
Decision trees
Instance-based
Neural networks
Model ensembles

25. ML Algorithms: by Evaluation
Evaluation: Quality measure for a model
25
Regression
Example metric: Root Mean Squared Error
RMSE =
Binary classification: confusion matrix
Accuracy: 8 + 971 -> 97,9%
Example: medical test
for a disease
Positive Negative
P
True
positives
TP
False
Negatives
FN
N
False
positives
FP
True
Negatives
TN
True
Class
Predicted class
Accuracy: Better evaluation metrics:
• Precision: 8 / (8 + 19)
• Recall: 8 / (8 + 2)

26. Optimization: how the algorithm ‘learns’, depends on representation and
evaluation
ML Algorithms: by Optimization
26
Greedy Search,
ex. of
combinatorial
optimization
Gradient Descent (or in general: Convex Optimization)
Linear Programming (or in general:
Constrained/Nonlinear Optimization)

27. Algorithms by Evaluation: Heuristics
• Hill climbing
• Simulated Annealing
• Nelder-Mead Simplex Method
• Artificial Bee Colony Optimization
• Genetic Algorithms
• Particle Swarm Optimization
• Ant Colony Optimization
27

28. Choice of ML-algorithm, considerations
• Size & Dimensionality of training set
• Computational efficiency
• Model building, no of parameters
• Eager vs lazy learning
• Online vs batch
• Interpretability
28

29. Evaluation: training vs test data
29
5-fold cross validation

30. Training error vs test error
30

31. Overfitting
31

32. Chebishev distance (L∞-norm: || ||∞ )
|| P – Q ||∞ = max( , )
Number of moves of a King on a chessboard ;-)
Manhattan distance (L1-norm: || ||1 )
|| P – Q ||1 = +
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7 8 9
Line through
(2,2) and (6,5)
Line y = 2
(between 2 and
6)
Vertical line x =
6 (between 2
and 5)
Distance metrics
Euclidean distance (L2-norm: || ||2 )
|| P – Q ||2 = (length of)
32
P
Q
Many more: Cosine distance, Edit distance (aka Levenshtein distance), …

33. Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
1. Demo, SAP ICN
2. Skill set for aspiring ML experts
3. Take-aways
33

34. Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
1. Demo, SAP ICN
2. Skill set for aspiring ML experts
3. Take-aways
34

35. So you want to be a Data Scientist?
35

36. CRISP-DM: data mining process
36

37. Hacking skills
• Programming languages:
• Libraries (examples):
• Tensorflow, Caffe, Theano, Keras
• SciPy & scikit-learn
• Spark MLLib (Scala/Java/Python)
37

38. Math skills: Statistics
38
Source: http://xkcd.com/552/

39. More math skills that may be needed…
39
Calculus Linear Algebra

40. Data Science for Business
• Focuses more on general principles
than specific algorithms
• Not math-heavy, does contain some
math
• O’Reilly link:
http://shop.oreilly.com/product/063692
0028918.do
• Book website: http://data-science-for-
biz.com/DSB/Home.html
40

41. Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
1. Demo, SAP ICN
2. Skill set for aspiring ML experts
3. Take-aways
41

42. What has NOT been covered
• Deep learning / Neural Networks
• Specifics of ML-algorithms
• Tools / Libraries / Code
• SAP Products, like HANA / Predictive Analytics / Vora / …
• Hardware
• …
42

43. Take-aways
• Goal of ML: generalize from training data (not optimization!!)
• Part of ‘Data Mining Process’, not a goal in and of itself
• No magic! Just some clever algorithms…
• Increasingly important non-technical aspects:
• Ethics
• Algorithmic transparency
43

44. Thank You
www.soapeople.com
info@soapeople.com
@SOAPEOPLE
Fred Verheul
Big Data Consultant
+31 6 3919 2986
fred.verheul@soapeople.com