This document provides an overview of machine learning. It discusses what machine learning is, including examples of common machine learning tasks and algorithms. The document also outlines the typical machine learning workflow, including data preparation, model building, evaluation, and optimization. Key takeaways are that the goal of machine learning is to generalize from training data, it is part of the broader data mining process, and non-technical issues like ethics and transparency are increasingly important.

1. Machine Learning
101
Fred Verheul

2. What we won’t cover…
• Deep learning / Neural Networks
• Specifics of ML-algorithms
• Tools / Libraries / Code
• SAP Products, like HANA / Predictive Analytics / Vora / …
• Ethics, algorithmic transparency & fairness
• Hardware
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3. Examples: Recommender systems
3

4. Examples, continued…
4
SPAM-
filtering
Handwriting
recognition

5. ML in the news: Deepmind’s AlphaGo
5

6. 6

7. Machine Learning
"Field of study that gives computers the ability to learn
without being explicitly programmed” (Arthur Samuel, 1959)
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8. What is Machine Learning?
8
Computer
Computer
Traditional Programming
Machine Learning
Data
Data
Program
Output
Program
Output

9. 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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10. 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

11. 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’

12. CRISP-DM: data mining process
12
ML
important
ML
important

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

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

16. 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)

17. 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)

18. Training error vs test error
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19. 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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20. 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
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21. Thank You
www.soapeople.com
info@soapeople.com
@SOAPEOPLE
Fred Verheul
Big Data Consultant
+31 6 3919 2986
fred.verheul@soapeople.com
@fredverheul