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

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Workshop on Machine Learning organized by Girls in Tech Luxembourg on April 2014.
Visit us at www.luxembourg.girlsintech.org

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

  1. 1. IS MACHINE LEARNING FORYOUR BUSINESS? Ekaterina Stambolieva Workshop #1 20/05/2014 1estambolieva@gmail.com / www.luxembourg.girlsintech.org
  2. 2. 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 2estambolieva@gmail.com / www.luxembourg.girlsintech.org
  3. 3. What is ML (Machine Learning)? 20/05/2014 3 Part of the field of Artificial Intelligence estambolieva@gmail.com / www.luxembourg.girlsintech.org
  4. 4. What is Machine Learning? 20/05/2014 4 Part of the field of Artificial Intelligence estambolieva@gmail.com / www.luxembourg.girlsintech.org
  5. 5. What is Machine Learning? 20/05/2014 5 Part of the field of Artificial Intelligence Predictive modelling estambolieva@gmail.com / www.luxembourg.girlsintech.org
  6. 6. 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
  7. 7. 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
  8. 8. 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
  9. 9. 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 9estambolieva@gmail.com / www.luxembourg.girlsintech.org
  10. 10. Where can ML be used? Banking • predicting the trade price of U.S Corporate bonds (introduced by Benchmark Solutions) 20/05/2014 10estambolieva@gmail.com / www.luxembourg.girlsintech.org
  11. 11. 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 11estambolieva@gmail.com / www.luxembourg.girlsintech.org
  12. 12. 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 12estambolieva@gmail.com / www.luxembourg.girlsintech.org
  13. 13. 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
  14. 14. 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
  15. 15. 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 15estambolieva@gmail.com / www.luxembourg.girlsintech.org
  16. 16. 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
  17. 17. Surprisingly also in Biology • protecting animals: algorithm to identify whales in the ocean based on recordings (introduced by Cornell University) 20/05/2014 17estambolieva@gmail.com / www.luxembourg.girlsintech.org
  18. 18. 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 18estambolieva@gmail.com / www.luxembourg.girlsintech.org
  19. 19. 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 19estambolieva@gmail.com / www.luxembourg.girlsintech.org
  20. 20. 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 20estambolieva@gmail.com / www.luxembourg.girlsintech.org
  21. 21. Surprisingly also in Business (more) • mobile social network analysis (introduced by Zendagui) 20/05/2014 21estambolieva@gmail.com / www.luxembourg.girlsintech.org
  22. 22. Surprisingly also in Business (more) • mobile social network analysis (introduced by Zendagui) • house-hold electricity consumption prediction (introduced by Novabase) 20/05/2014 22estambolieva@gmail.com / www.luxembourg.girlsintech.org
  23. 23. 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 23estambolieva@gmail.com / www.luxembourg.girlsintech.org
  24. 24. Surprisingly also in 20/05/2014 24estambolieva@gmail.com / www.luxembourg.girlsintech.org
  25. 25. 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 25estambolieva@gmail.com / www.luxembourg.girlsintech.org
  26. 26. 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 26estambolieva@gmail.com / www.luxembourg.girlsintech.org
  27. 27. What is data? 20/05/2014 27estambolieva@gmail.com / www.luxembourg.girlsintech.org
  28. 28. What is data? 20/05/2014 28estambolieva@gmail.com / www.luxembourg.girlsintech.org
  29. 29. What is data? • How is it related to Machine Learning? 20/05/2014 29estambolieva@gmail.com / www.luxembourg.girlsintech.org
  30. 30. 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
  31. 31. What is data? 20/05/2014 31estambolieva@gmail.com / www.luxembourg.girlsintech.org
  32. 32. What is data? 20/05/2014 32estambolieva@gmail.com / www.luxembourg.girlsintech.org
  33. 33. 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 33estambolieva@gmail.com / www.luxembourg.girlsintech.org
  34. 34. Types of ML 20/05/2014 34estambolieva@gmail.com / www.luxembourg.girlsintech.org
  35. 35. Types of ML 20/05/2014 35 How would you win a game of chess? estambolieva@gmail.com / www.luxembourg.girlsintech.org
  36. 36. Type 1: Supervised 20/05/2014 36 learns from labelled data estambolieva@gmail.com / www.luxembourg.girlsintech.org
  37. 37. 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
  38. 38. 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
  39. 39. Type 1: Supervised 20/05/2014 39estambolieva@gmail.com / www.luxembourg.girlsintech.org
  40. 40. Type 1: Supervised 20/05/2014 40 Decision Boundary of Predictive Model estambolieva@gmail.com / www.luxembourg.girlsintech.org
  41. 41. Type 1: Supervised 20/05/2014 41estambolieva@gmail.com / www.luxembourg.girlsintech.org
  42. 42. Type 1: Supervised 20/05/2014 42estambolieva@gmail.com / www.luxembourg.girlsintech.org
  43. 43. Type 2: Unsupervised 20/05/2014 43 we have unlabelled data estambolieva@gmail.com / www.luxembourg.girlsintech.org
  44. 44. 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
  45. 45. 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
  46. 46. 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
  47. 47. Type 2: Unsupervised 20/05/2014 47estambolieva@gmail.com / www.luxembourg.girlsintech.org
  48. 48. Type 2: Unsupervised 20/05/2014 48estambolieva@gmail.com / www.luxembourg.girlsintech.org
  49. 49. Type 2: Unsupervised 20/05/2014 49 We cannot say to Google News: find me X political stories andY sports ones estambolieva@gmail.com / www.luxembourg.girlsintech.org
  50. 50. Type 3: Online 20/05/2014 50 learn example by example estambolieva@gmail.com / www.luxembourg.girlsintech.org
  51. 51. Type 3: Online 20/05/2014 51estambolieva@gmail.com / www.luxembourg.girlsintech.org
  52. 52. Type 3: Online 20/05/2014 52 Blue decision boundary is the true decision boundary estambolieva@gmail.com / www.luxembourg.girlsintech.org
  53. 53. Type 3: Online 20/05/2014 53 Blue decision boundary is the true decision boundary estambolieva@gmail.com / www.luxembourg.girlsintech.org
  54. 54. Type 3: Online 20/05/2014 54 Blue decision boundary is the true decision boundary estambolieva@gmail.com / www.luxembourg.girlsintech.org
  55. 55. 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 55estambolieva@gmail.com / www.luxembourg.girlsintech.org
  56. 56. Who can you hire to do ML? 20/05/2014 56 Anyone can do the job estambolieva@gmail.com / www.luxembourg.girlsintech.org
  57. 57. Who can you hire to do ML? 20/05/2014 57 Anyone can do the job ..but.. estambolieva@gmail.com / www.luxembourg.girlsintech.org
  58. 58. 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
  59. 59. Who can you hire to do ML? 20/05/2014 59 Desired skills: 1. Mathematics estambolieva@gmail.com / www.luxembourg.girlsintech.org
  60. 60. Who can you hire to do ML? 20/05/2014 60 Desired skills: 1. Mathematics 2. Programming estambolieva@gmail.com / www.luxembourg.girlsintech.org
  61. 61. 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
  62. 62. 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
  63. 63. 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
  64. 64. 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
  65. 65. 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 65estambolieva@gmail.com / www.luxembourg.girlsintech.org
  66. 66. MLTools • 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
  67. 67. MLTools • 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
  68. 68. 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 68estambolieva@gmail.com / www.luxembourg.girlsintech.org
  69. 69. Practical Example What is your problem? 20/05/2014 69estambolieva@gmail.com / www.luxembourg.girlsintech.org
  70. 70. 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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