Overview talk on Open Data, Big Data and Machine Learning concepts. Presented at the Urban Design Hackathon of the European Maker Week 2016.

1. Open Data, Big Data
and Machine Learning
Steven Van Vaerenbergh
Universidad de Cantabria
May 31, 2016
#EMWeek16 - Santander

2. About me
Researcher in machine learning
gtas.unican.es/people/steven
Open Data, Big Data and Machine Learning 2
twitter.com/steven2358
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3. 1. Open Data
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4. Denmark’s Open Address Data Set
• Making public data
“free of charge”
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Period Benefits Costs Return on
Investment
2004-2009 (including
setup)
>€60M ~€2M 22:1
2010 (steady state) ~€14M €0.2M 70:1
Source: http://odimpact.org/static/files/case-study-denmark.pdf
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7. Open Data in Santander
• Santander Datos Abiertos http://datos.santander.es/
• FIWARE lab: https://www.fiware.org/lab/
• FIWARE Academy: http://edu.fiware.org
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8. Open data
• “A data set is open if it is available under a free
license to everyone”.
• Providers: Governments, public services,
companies, individuals.
• Tendency: Many data providers stop making apps
and leave this to third parties.
Open data improves transparency
Not all data should be open though (privacy)
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9. 2. Big Data
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10. Big Data
• Scientific definition: “Data sets that are so large
that traditional data processing techniques cannot
be applied to them”.
• Terabytes, Petabytes, Exabytes, etc.
• “Big Data” is also used to refer to novel analysis
techniques for such data.
• Typically not open data.
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11. Big Data = Data Science with Lots of Data
Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
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12. Big Data
• Many frameworks are being developed:
• Apache Hadoop
• Apache Mahout
• NoSQL
• Caution: The science behind big data is in its infancy.
E.g. most methods are not able to produce error
bars, which is paramount in many applications.
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13. Big Data
• Media and press often use “big data” to refer to
data science even if the amount of data is relatively
small.
 “Big data” is often simply a marketing term.
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14. Big Data = Data Science with Lots of Data
Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
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?
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15. 3. Machine Learning
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16. Traditional Machine Intelligence
• Example: decision tree for determining access
Program consists of a set of rules (logic)
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Input: age, gender, occupation,… Permission to enter Juanito’s tree house?
Yes No No
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17. Traditional Machine Intelligence
• Example: decision tree for digit recognition
Set of rules is very hard to design by hand
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Input: images (MNIST) Which digit is represented?
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18. Traditional Machine Intelligence
• Example: decision tree for image recognition
Set of rules is impossible to design by hand
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Input: images (CIFAR10) What does the image represent?
Correct
answer
?
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19. Machine Learning
• Solution: Let the program itself determine its
internal set of rules.
• Provide the program with inputs and correct
answers for these rules, and let it “learn”.
“Machine Learning is the study of
computer algorithms that
improve their performance
on a task automatically
through experience.”
- Tom Mitchell
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20. Open Data, Big Data and Machine Learning 20
Traditional Machine Intelligence
Computer
Input
Program
Output
Machine Learning (ML)
ML algorithm
Input
Output
Program
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21. Machine Learning Applications
• Spam filters detect unsolicited emails
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SPAM
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22. Machine Learning Applications
• Biomedicine: pattern detection in images
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23. Machine Learning Applications
• Computer Vision: Kinect body tracking
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24. Machine Learning Applications
• Natural Language Processing (NLP)
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25. Machine Learning Applications
1996: IBM’s Deep Blue
(Chess)
• Intelligence based on
manually-entered rules
2016: Google Deepmind’s
AlphaGo (Go)
• Program learns
autonomously
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26. Machine Learning Applications
• Human activity recognition
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Running
Walking
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27. Internal representation
How to represent the function from input to output?
• Neural networks
• Support vector machines
• Sets of rules / Logic programs
• Bayes/Markov nets
• Model ensembles
• Decision trees
• Etc.
Neural net demo: http://playground.tensorflow.org/
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28. Tools and Frameworks
• Machine learning toolkits:
• Scikit Learn (Python) http://scikit-learn.org/
• Weka (Java) http://www.cs.waikato.ac.nz/ml/weka/
• Shogun http://www.shogun-toolbox.org/
• Cloud-based machine learning
• IBM Watson https://developer.ibm.com/watson/
• Amazon ML https://aws.amazon.com/machine-learning/
• Microsoft Azure ML https://azure.microsoft.com/en-
us/services/machine-learning/
• Google Cloud ML
https://cloud.google.com/products/machine-learning/
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29. Takeaways
• Open data, big data and machine learning are
components of the current technological wave that
resembles an industrial revolution.
• Big data requires a rigorous scientific engineering
framework that is currently unfinished.
• Machine learning algorithms create intelligent
programs by automatically learning from example
data.
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30. Join us on Meetup
Meetup group for people
in Santander & Cantabria
interested in everything
related to data science
www.meetup.com/Data-Science-Santander
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