Is Machine learning for your business? - Girls in Tech Luxembourg

IS MACHINE LEARNING FOR YOUR BUSINESS?

Ekaterina Stambolieva

Workshop #1

20/05/2014 1 [email protected] / www.luxembourg.girlsintech.org

lu.linkedin.com/pub/ekaterina-stambolieva/45/8aa/2a0/

mailto:[email protected]

http://www.luxembourg.girlsintech.org/

Outline

1. What is ML (= Machine Learning)

2. Where is ML used?

3. What is data?

4. Types of ML

5. Who can you hire to do ML for you?

6. What tools can you use for ML?




What is ML (Machine Learning)?

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Part of the field of Artificial Intelligence

[email protected] / www.luxembourg.girlsintech.org



What is Machine Learning?

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Predictive modelling





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What ML does is it gives individuals the tools to help the machines learn something by themselves given that

this knowledge is difficult to be decoded by the humans





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What ML does is it gives individuals the tools to help the machines learn something by themselves given that

this knowledge is difficult to be decoded by the humans

ML is used in applications that humans cannot handle by hand




And a little something that is quite exciting….

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https://www.youtube.com/watch?v=bp9KBrH8H04


https://www.youtube.com/watch?v=bp9KBrH8H04



Outline



3. What is data?

4. Types of ML






Where can ML be used?

Banking

• predicting the trade price of U.S Corporate bonds (introduced by Benchmark Solutions)





Banking


• credit card fraud prediction (introduced by Feedzai)





Banking



• bankruptcy prediction (currently a research topic at uni.lu)





Banking




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Is ML used only in banking and self-driving (unmanned) vehicles?





Banking




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Is ML used only in banking and self-driving (unmanned) vehicles?





Medicine

• in cancer research: predicting tumor state - benign or malignant?

• in HIV research

• in early stage of disease detection

• predicting emergency room wait time





Medicine

• in cancer research: predicting tumor state - benign or malignant?

• in HIV research

• in early stage of disease detection

• predicting emergency room wait time

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It is getting interesting – ML can help us improve our health




Surprisingly also in

Biology

• protecting animals: algorithm to identify whales in the ocean based on recordings

(introduced by Cornell University)





Biology



Business

• predictive analysis of whether a product launch will be successful





Biology



Business

• predictive analysis of whether a product launch will be successful (introduced by hack/reduce & Dunnhumby)

• predict house prices (Andrew NG talks a lot about that in his ML online course in courser.org)





Biology



Business

• predictive analysis of whether a product launch will be successful (introduced by hack/reduce & Dunnhumby)

• predict house prices

• predict which new questions will be closed (introduced by stackoverflow)





Business (more)

• mobile social network analysis (introduced by Zendagui)





Business (more)


• house-hold electricity consumption prediction (introduced by Novabase)





Business (more)



Something more familiar:

• Recommendation system (well-known because Amazon & Netflix)





Business (more)



Something more familiar:

• recommendation system (well-known because Amazon & Netflix)

• Google’s search engine

• iPhoto face prediction

• spam filters




Outline



3. What is data?

4. Types of ML






What is data?




What is data?

• How is it related to Machine Learning?




What is data?

• How is it related to Machine Learning?

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We want to learn a predictive model from the data




What is data?




Outline



3. What is data?

4. Types of ML






Types of ML




Types of ML

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How would you win a game of chess?




Type 1: Supervised

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learns from labelled data




Type 1: Supervised

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?

Predict whether a cancerous formation is malignant or benign.




Type 1: Supervised

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?

Predict whether a cancerous formation is malignant or benign. How: by looking at the data (size of tumor for different patients)




Type 1: Supervised




Type 1: Supervised

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Decision Boundary of Predictive Model




Type 1: Supervised




Type 2: Unsupervised

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we have unlabelled data





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we do not know what we want to learn





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?





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?

So we give the data to the algorithm and see what it will tell us about it





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We cannot say to Google News: find me X political stories and Y sports ones




Type 3: Online

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learn example by example




Type 3: Online




Type 3: Online

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Blue decision boundary is the true decision boundary




Type 3: Online

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Type 3: Online

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Outline



3. What is data?

4. Types of ML






Who can you hire to do ML?

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Anyone can do the job





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Anyone can do the job ..but..





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Anyone can do the job ..but..

Not all will do it well





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Desired skills: 1. Mathematics





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Desired skills: 1. Mathematics 2. Programming





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Desired skills: 1. Mathematics 2. Programming

University Degree Equivalent: 1. Mathematics 2. Computer Science 3. Physics/Signal

Processing 4. Engineering 5. ?





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Mathematics Degree: - look for some programming courses (Logical programming, Functional Programming, others.)







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Computer Science Degree: - look for some mathematics courses (Mathematical Analysis, Discrete Mathematics, others.)







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Physics/Engineering Degree: - look for some programming courses






Outline



3. What is data?

4. Types of ML






ML Tools

• Weka*

• Octave**

• Matlab***

• Stand-alone libraries for different programming languages: • libsvm**** for Java for example

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* weka http://www.cs.waikato.ac.nz/ml/weka/ ** octave https://www.gnu.org/software/octave *** matlab www.mathworks.co.uk/products/matlab **** libsvm www.csie.ntu.edu.tw/~cjlin/libsvm


http://www.cs.waikato.ac.nz/ml/weka/

https://www.gnu.org/software/octave


http://www.mathworks.co.uk/products/matlab



ML Tools

• Weka* has GUI

• Octave**

• Matlab***

• Stand-alone libraries for different programming languages: • libsvm**** for Java for example

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* weka http://www.cs.waikato.ac.nz/ml/weka/ ** octave https://www.gnu.org/software/octave *** matlab www.mathworks.co.uk/products/matlab **** libsvm www.csie.ntu.edu.tw/~cjlin/libsvm


http://www.cs.waikato.ac.nz/ml/weka/



http://www.mathworks.co.uk/products/matlab



Practical Example

Problem: Help democracy reach the poor population in Africa

Solution to the Problem: Give the PM representatives written texts with verbal requests voiced by the population

Data: Spoken (in different dialects – Baramba, Oualoff) audio recordings

Goal: Learn to differentiate dialects

The missing piece: What else do we need to do? Is the goal complete? Can ML help?




Practical Example

What is your problem?




The End

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20/05/2014 [email protected] / www.luxembourg.girlsintech.org 70

* When translating ‘Thank you’ here: http://www.binarytranslator.com/index.php



Is Machine learning for your business? - Girls in Tech Luxembourg

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