is machine learning for your business? - girls in tech luxembourg
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IS MACHINE LEARNING FOR YOUR BUSINESS?
Ekaterina Stambolieva
Workshop #1
20/05/2014 1 estambolieva@gmail.com / 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?
20/05/2014 2 estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is ML (Machine Learning)?
20/05/2014 3
Part of the field of Artificial Intelligence
estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is Machine Learning?
20/05/2014 4
Part of the field of Artificial Intelligence
estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is Machine Learning?
20/05/2014 5
Part of the field of Artificial Intelligence
Predictive modelling
estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is Machine Learning?
20/05/2014 6
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
estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is Machine Learning?
20/05/2014 7
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
estambolieva@gmail.com / www.luxembourg.girlsintech.org
And a little something that is quite exciting….
20/05/2014 8
https://www.youtube.com/watch?v=bp9KBrH8H04
estambolieva@gmail.com / 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?
20/05/2014 9 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Where can ML be used?
Banking
• predicting the trade price of U.S Corporate bonds (introduced by Benchmark Solutions)
20/05/2014 10 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Where can ML be used?
Banking
• predicting the trade price of U.S Corporate bonds (introduced by Benchmark Solutions)
• credit card fraud prediction (introduced by Feedzai)
20/05/2014 11 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Where can ML be used?
Banking
• predicting the trade price of U.S Corporate bonds (introduced by Benchmark Solutions)
• credit card fraud prediction (introduced by Feedzai)
• bankruptcy prediction (currently a research topic at uni.lu)
20/05/2014 12 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Where can ML be used?
Banking
• predicting the trade price of U.S Corporate bonds (introduced by Benchmark Solutions)
• credit card fraud prediction (introduced by Feedzai)
• bankruptcy prediction (currently a research topic at uni.lu)
20/05/2014 13
Is ML used only in banking and self-driving (unmanned) vehicles?
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Where can ML be used?
Banking
• predicting the trade price of U.S Corporate bonds (introduced by Benchmark Solutions)
• credit card fraud prediction (introduced by Feedzai)
• bankruptcy prediction (currently a research topic at uni.lu)
20/05/2014 14
Is ML used only in banking and self-driving (unmanned) vehicles?
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Where can ML be used?
Medicine
• in cancer research: predicting tumor state - benign or malignant?
• in HIV research
• in early stage of disease detection
• predicting emergency room wait time
20/05/2014 15 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Where can ML be used?
Medicine
• in cancer research: predicting tumor state - benign or malignant?
• in HIV research
• in early stage of disease detection
• predicting emergency room wait time
20/05/2014 16
It is getting interesting – ML can help us improve our health
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Surprisingly also in
Biology
• protecting animals: algorithm to identify whales in the ocean based on recordings
(introduced by Cornell University)
20/05/2014 17 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Surprisingly also in
Biology
• protecting animals: algorithm to identify whales in the ocean based on recordings
(introduced by Cornell University)
Business
• predictive analysis of whether a product launch will be successful
20/05/2014 18 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Surprisingly also in
Biology
• protecting animals: algorithm to identify whales in the ocean based on recordings
(introduced by Cornell University)
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)
20/05/2014 19 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Surprisingly also in
Biology
• protecting animals: algorithm to identify whales in the ocean based on recordings
(introduced by Cornell University)
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)
20/05/2014 20 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Surprisingly also in
Business (more)
• mobile social network analysis (introduced by Zendagui)
20/05/2014 21 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Surprisingly also in
Business (more)
• mobile social network analysis (introduced by Zendagui)
• house-hold electricity consumption prediction (introduced by Novabase)
20/05/2014 22 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Surprisingly also in
Business (more)
• mobile social network analysis (introduced by Zendagui)
• house-hold electricity consumption prediction (introduced by Novabase)
Something more familiar:
• Recommendation system (well-known because Amazon & Netflix)
20/05/2014 23 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Surprisingly also in
20/05/2014 24 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Surprisingly also in
Business (more)
• mobile social network analysis (introduced by Zendagui)
• house-hold electricity consumption prediction (introduced by Novabase)
Something more familiar:
• recommendation system (well-known because Amazon & Netflix)
• Google’s search engine
• iPhoto face prediction
• spam filters
20/05/2014 25 estambolieva@gmail.com / 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?
20/05/2014 26 estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is data?
20/05/2014 27 estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is data?
20/05/2014 28 estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is data?
• How is it related to Machine Learning?
20/05/2014 29 estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is data?
• How is it related to Machine Learning?
20/05/2014 30
We want to learn a predictive model from the data
estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is data?
20/05/2014 31 estambolieva@gmail.com / www.luxembourg.girlsintech.org
What is data?
20/05/2014 32 estambolieva@gmail.com / 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?
20/05/2014 33 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Types of ML
20/05/2014 34 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Types of ML
20/05/2014 35
How would you win a game of chess?
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 36
learns from labelled data
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 37
learns from labelled data
?
Predict whether a cancerous formation is malignant or benign.
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 38
learns from labelled data
?
Predict whether a cancerous formation is malignant or benign. How: by looking at the data (size of tumor for different patients)
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 39 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 40
Decision Boundary of Predictive Model
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 41 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 42 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 43
we have unlabelled data
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 44
we have unlabelled data
we do not know what we want to learn
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 45
we have unlabelled data
we do not know what we want to learn
?
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 46
we have unlabelled data
we do not know what we want to learn
?
So we give the data to the algorithm and see what it will tell us about it
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 47 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 48 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 49
We cannot say to Google News: find me X political stories and Y sports ones
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 3: Online
20/05/2014 50
learn example by example
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 3: Online
20/05/2014 51 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 3: Online
20/05/2014 52
Blue decision boundary is the true decision boundary
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 3: Online
20/05/2014 53
Blue decision boundary is the true decision boundary
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Type 3: Online
20/05/2014 54
Blue decision boundary is the true decision boundary
estambolieva@gmail.com / 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?
20/05/2014 55 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 56
Anyone can do the job
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 57
Anyone can do the job ..but..
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 58
Anyone can do the job ..but..
Not all will do it well
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 59
Desired skills: 1. Mathematics
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 60
Desired skills: 1. Mathematics 2. Programming
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 61
Desired skills: 1. Mathematics 2. Programming
University Degree Equivalent: 1. Mathematics 2. Computer Science 3. Physics/Signal
Processing 4. Engineering 5. ?
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 62
Mathematics Degree: - look for some programming courses (Logical programming, Functional Programming, others.)
University Degree Equivalent: 1. Mathematics 2. Computer Science 3. Physics/Signal
Processing 4. Engineering 5. ?
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 63
Computer Science Degree: - look for some mathematics courses (Mathematical Analysis, Discrete Mathematics, others.)
University Degree Equivalent: 1. Mathematics 2. Computer Science 3. Physics/Signal
Processing 4. Engineering 5. ?
estambolieva@gmail.com / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 64
Physics/Engineering Degree: - look for some programming courses
University Degree Equivalent: 1. Mathematics 2. Computer Science 3. Physics/Signal
Processing 4. Engineering 5. ?
estambolieva@gmail.com / 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?
20/05/2014 65 estambolieva@gmail.com / www.luxembourg.girlsintech.org
ML Tools
• Weka*
• Octave**
• Matlab***
• Stand-alone libraries for different programming languages: • libsvm**** for Java for example
20/05/2014 66
* 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
estambolieva@gmail.com / www.luxembourg.girlsintech.org
ML Tools
• Weka* has GUI
• Octave**
• Matlab***
• Stand-alone libraries for different programming languages: • libsvm**** for Java for example
20/05/2014 67
* 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
estambolieva@gmail.com / www.luxembourg.girlsintech.org
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?
20/05/2014 68 estambolieva@gmail.com / www.luxembourg.girlsintech.org
Practical Example
What is your problem?
20/05/2014 69 estambolieva@gmail.com / www.luxembourg.girlsintech.org
The End
01010100 01101000 01100001 01101110 01101011
00100000
01111001 01101111 01110101*
20/05/2014 estambolieva@gmail.com / www.luxembourg.girlsintech.org 70
* When translating ‘Thank you’ here: http://www.binarytranslator.com/index.php
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