5. Sweet spot for Machine Learning
• It’s impossible to write down the rules in code:
• Too many rules
• Too many factors influencing the rules
• Too finely tuned
• We just don’t know the rules (image recognition)
• Lots of labeled data (examples) available (e.g. historical data)
5
6. Basic Machine Learning ‘workflow’
6
Feature
Vectors
Training
data
Labels
Machine
Learning
Algorithm
Feature
Vectors
New data Prediction
Training Phase
Operational Phase
Predictive
Model
7. Training Phase in more detail
7
Raw data
Data
preparation Feature
Vectors
Training
Data
Test
data
Model Building
(by ML
algorithm)
Model
Evaluation
Predictive
Model
Feedback loop
data cleansing
data transformation
normalization
feature extraction
aka
‘learning’
8. Examples of ML tasks
Supervised learning
Regression
target is numeric
Classification
target is categorical
8
Unsupervised learning
Clustering
Dimensionality
reduction
10. ML Algorithms: by Representation
Collection of candidate models/programs, aka hypothesis space
10
Decision trees
Instance-based
Neural networks
Model ensembles
11. ML Algorithms: by Evaluation
Evaluation: Quality measure for a model
11
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)
12. Optimization: how the algorithm ‘learns’, depends on representation and
evaluation
ML Algorithms: by Optimization
12
Greedy Search,
ex. of
combinatorial
optimization
Gradient Descent (or in general: Convex Optimization)
Linear Programming (or in general:
Constrained/Nonlinear Optimization)
14. 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
14
15. What has NOT been covered (1)
• Deep learning / Neural Networks
• Covered in other presentations at DKOM
• Also recommended for further reading (deep dive):
• http://neuralnetworksanddeeplearning.com/index.html
• Specifics of ML-algorithms
• All over the internet… e.g. at http://machinelearningmastery.com/
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16. What has NOT been covered (2)
• Libraries (examples):
• Tensorflow, Caffe, Theano, Keras
• SciPy & scikit-learn
• Spark MLLib (Scala/Java/Python)
• Programming languages:
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17. What has NOT been covered (3)
• SAP products:
• SAP HANA, SAP HANA Vora, SAP
BO Predictive Analytics(!), HCP
Predictive Services
• New machine learning platform
• Hardware
• Nvidia talk about GPUs
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18. What has NOT been covered (4)
• Ethics and algorithmic
transparency:
18
19. What has NOT been covered (5)
• The Data Science &
Data Mining Process:
19
20. What has NOT been covered (6)
• How to integrate ML into your business
application
• I hope SAP is figuring that out as we speak ;-)
• Have a look at SAP Predictive Analytics Integrator
• https://help.sap.com/pai
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21. Take-aways
• Goal of ML: generalize from training data (not optimization!!)
• No magic! Just some clever algorithms…
• Increasingly important non-technical aspects:
• Ethics
• Algorithmic transparency
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