Open Data, Big Data and Machine Learning

Steven Van VaerenberghUniversidad de Cantabria

May 31, 2016#EMWeek16 - Santander

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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1. Open Data

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Denmark’s Open Address Data Set

• Making public data“free of charge”

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Period Benefits Costs Return onInvestment

2004-2009 (includingsetup)

>€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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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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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 transparencyNot all data should be open though (privacy)

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2. Big Data

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Big Data

• Scientific definition: “Data sets that are so largethat traditional data processing techniques cannotbe 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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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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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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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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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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3. Machine Learning

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Traditional Machine Intelligence

• Example: decision tree for determining access

Program consists of a set of rules (logic)Open Data, Big Data and Machine Learning 16

Input: age, gender, occupation,… Permission to enter Juanito’s tree house?

Yes No No

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Traditional Machine Intelligence

• Example: decision tree for digit recognition

Set of rules is very hard to design by handOpen Data, Big Data and Machine Learning 17

Input: images (MNIST) Which digit is represented?

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Traditional Machine Intelligence

• Example: decision tree for image recognition

Set of rules is impossible to design by handOpen Data, Big Data and Machine Learning 18

Input: images (CIFAR10) What does the image represent?

Correctanswer

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Machine Learning

• Solution: Let the program itself determine itsinternal set of rules.

• Provide the program with inputs and correctanswers for these rules, and let it “learn”.

“Machine Learning is the study ofcomputer algorithms that

improve their performanceon a task automaticallythrough experience.”

- Tom Mitchell

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Traditional Machine Intelligence

ComputerInput

ProgramOutput

Machine Learning (ML)

ML algorithmInput

OutputProgram

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Machine Learning Applications

• Spam filters detect unsolicited emails

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SPAM

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Machine Learning Applications

• Biomedicine: pattern detection in images

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Machine Learning Applications

• Computer Vision: Kinect body tracking

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Machine Learning Applications

• Natural Language Processing (NLP)

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Machine Learning Applications

1996: IBM’s Deep Blue (Chess)• Intelligence based on

manually-entered rules

2016: Google Deepmind’sAlphaGo (Go)• Program learns

autonomously

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Machine Learning Applications• Human activity recognition

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Running

Walking

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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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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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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 engineeringframework that is currently unfinished.

• Machine learning algorithms create intelligentprograms by automatically learning from exampledata.

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Join us on Meetup

Meetup group for peoplein Santander & Cantabriainterested in everythingrelated to data science

www.meetup.com/Data-Science-Santander

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