Date post: | 20-Aug-2015 |
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IS MACHINE LEARNING FOR YOUR BUSINESS?
Ekaterina Stambolieva
Workshop #1
20/05/2014 1 [email protected] / 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 [email protected] / www.luxembourg.girlsintech.org
What is ML (Machine Learning)?
20/05/2014 3
Part of the field of Artificial Intelligence
[email protected] / www.luxembourg.girlsintech.org
What is Machine Learning?
20/05/2014 4
Part of the field of Artificial Intelligence
[email protected] / www.luxembourg.girlsintech.org
What is Machine Learning?
20/05/2014 5
Part of the field of Artificial Intelligence
Predictive modelling
[email protected] / 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
[email protected] / 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
[email protected] / www.luxembourg.girlsintech.org
And a little something that is quite exciting….
20/05/2014 8
https://www.youtube.com/watch?v=bp9KBrH8H04
[email protected] / 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 [email protected] / 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 [email protected] / 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 [email protected] / 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 [email protected] / 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?
[email protected] / 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?
[email protected] / 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 [email protected] / 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
[email protected] / 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 [email protected] / 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 [email protected] / 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 [email protected] / 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 [email protected] / www.luxembourg.girlsintech.org
Surprisingly also in
Business (more)
• mobile social network analysis (introduced by Zendagui)
20/05/2014 21 [email protected] / 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 [email protected] / 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 [email protected] / www.luxembourg.girlsintech.org
Surprisingly also in
20/05/2014 24 [email protected] / 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 [email protected] / 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 [email protected] / www.luxembourg.girlsintech.org
What is data?
20/05/2014 27 [email protected] / www.luxembourg.girlsintech.org
What is data?
20/05/2014 28 [email protected] / www.luxembourg.girlsintech.org
What is data?
• How is it related to Machine Learning?
20/05/2014 29 [email protected] / 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
[email protected] / www.luxembourg.girlsintech.org
What is data?
20/05/2014 31 [email protected] / www.luxembourg.girlsintech.org
What is data?
20/05/2014 32 [email protected] / 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 [email protected] / www.luxembourg.girlsintech.org
Types of ML
20/05/2014 34 [email protected] / www.luxembourg.girlsintech.org
Types of ML
20/05/2014 35
How would you win a game of chess?
[email protected] / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 36
learns from labelled data
[email protected] / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 37
learns from labelled data
?
Predict whether a cancerous formation is malignant or benign.
[email protected] / 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)
[email protected] / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 39 [email protected] / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 40
Decision Boundary of Predictive Model
[email protected] / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 41 [email protected] / www.luxembourg.girlsintech.org
Type 1: Supervised
20/05/2014 42 [email protected] / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 43
we have unlabelled data
[email protected] / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 44
we have unlabelled data
we do not know what we want to learn
[email protected] / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 45
we have unlabelled data
we do not know what we want to learn
?
[email protected] / 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
[email protected] / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 47 [email protected] / www.luxembourg.girlsintech.org
Type 2: Unsupervised
20/05/2014 48 [email protected] / 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
[email protected] / www.luxembourg.girlsintech.org
Type 3: Online
20/05/2014 50
learn example by example
[email protected] / www.luxembourg.girlsintech.org
Type 3: Online
20/05/2014 51 [email protected] / www.luxembourg.girlsintech.org
Type 3: Online
20/05/2014 52
Blue decision boundary is the true decision boundary
[email protected] / www.luxembourg.girlsintech.org
Type 3: Online
20/05/2014 53
Blue decision boundary is the true decision boundary
[email protected] / www.luxembourg.girlsintech.org
Type 3: Online
20/05/2014 54
Blue decision boundary is the true decision boundary
[email protected] / 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 [email protected] / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 56
Anyone can do the job
[email protected] / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 57
Anyone can do the job ..but..
[email protected] / 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
[email protected] / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 59
Desired skills: 1. Mathematics
[email protected] / www.luxembourg.girlsintech.org
Who can you hire to do ML?
20/05/2014 60
Desired skills: 1. Mathematics 2. Programming
[email protected] / 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. ?
[email protected] / 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. ?
[email protected] / 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. ?
[email protected] / 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. ?
[email protected] / 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 [email protected] / 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
[email protected] / 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
[email protected] / 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 [email protected] / www.luxembourg.girlsintech.org
Practical Example
What is your problem?
20/05/2014 69 [email protected] / www.luxembourg.girlsintech.org
The End
01010100 01101000 01100001 01101110 01101011
00100000
01111001 01101111 01110101*
20/05/2014 [email protected] / www.luxembourg.girlsintech.org 70
* When translating ‘Thank you’ here: http://www.binarytranslator.com/index.php