This document provides an introduction to machine learning, including: - Machine learning allows computers to learn without being explicitly programmed by using data to find patterns and make predictions. - There are two main phases: the training phase where a model is built using sample data, and the operational phase where the model is used to make predictions on new data. - Common machine learning tasks include supervised learning techniques like regression and classification, as well as unsupervised learning techniques like clustering and dimensionality reduction. - The document outlines different machine learning algorithms categorized by their representation, evaluation, and optimization methods, but does not cover specifics of individual algorithms.

1. Machine Learning 101
Fred Verheul

2. Machine Learning
"Field of study that gives computers the ability to learn
without being explicitly programmed” (Arthur Samuel, 1959)
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3. What is Machine Learning?
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Computer
Computer
Traditional Programming
Machine Learning
Data
Data
Program
Output
Program
Output

4. Prediction is hard…
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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)
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6. Basic Machine Learning ‘workflow’
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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
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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
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Unsupervised learning
Clustering
Dimensionality
reduction

9. Modeling: so many algorithms…
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10. ML Algorithms: by Representation
Collection of candidate models/programs, aka hypothesis space
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Decision trees
Instance-based
Neural networks
Model ensembles

11. ML Algorithms: by Evaluation
Evaluation: Quality measure for a model
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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
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Greedy Search,
ex. of
combinatorial
optimization
Gradient Descent (or in general: Convex Optimization)
Linear Programming (or in general:
Constrained/Nonlinear Optimization)

13. Training error vs test error
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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
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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:
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19. What has NOT been covered (5)
• The Data Science &
Data Mining Process:
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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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22. Thank You
www.soapeople.com
info@soapeople.com
@SOAPEOPLE
Fred Verheul
Big Data Consultant
+31 6 3919 2986
fred.verheul@soapeople.com