ML Session n°1

ML Session n°1


Adrien Couque

January 20, 2017


  1. ML: concepts January 2017

  2. Format Slack channel : #ml-courses • Today : concepts •

    08 Feb : understanding a ML project : what are the good questions? • 20 Feb and following : ◦ more technical sessions ◦ optional “homework” between sessions (small projects) ◦ current plan : 7 technical sessions ◦ goal : be able to work on ML projects autonomously
  3. Machine Learning

  4. Hierarchy

  5. Explaining Machine Learning Machine learning is the idea that there

    are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.
  6. Machine Learning vs Statistics ? They are both concerned with

    the same question: how do we learn from data? Statistics Machine Learning Estimation Learning Classifier Hypothesis Data Point Example/ Instance Regression Supervised Learning Classification Supervised Learning Covariate Feature Response Label
  7. Inputs Raw data Multimedia (sound, pictures, videos) Language

  8. ML vs “traditional” methods

  9. Minksy’s Multiplicity (1960) Crucial parts for problem solving : •

    Induction • Planning • Search, knowledge representation • Pattern recognition • Learning Components needed to get to human-level AI
  10. Minksy’s Multiplicity (1960) Crucial parts for problem solving : •

    Induction • Planning • Search, knowledge representation • Pattern recognition • Learning Components needed to get to human-level AI
  11. Topics for Machine Learning • Self-driving cars • Human interaction

    : ◦ Handwriting ◦ Speech ◦ Natural language • OCR • Image recognition • Information retrieval • Artificial personal assistants • Recommendations systems • Drones • Game playing • ...
  12. Subdomains

  13. Subdomains of Machine Learning Machine Learning Supervised Learning Unsupervised Learning

    Reinforcement Learning
  14. Supervised learning

  15. Supervised learning Labelled data Classification Regression

  16. Demo: Intro to Machine Learning visual-intro-to- machine-learning-part-1/

  17. Unsupervised learning

  18. Unsupervised learning

  19. Reinforcement learning

  20. Reinforcement learning

  21. Reinforcement Learning: OpenAI

  22. First intuitions

  23. Linear regression

  24. Intuitions from linear regression • algorithm is generic, results depends

    on data • system is both the algorithm and the data • only as good as your data • starts with a hypothesis about how we can represent the data (for linear regression : a straight line) • can deal poorly with outliers • lots of calculation to learn, but very fast to apply (can run on mobile)
  25. None
  26. Tools

  27. Artificial Neural Networks (ANN) Each node does a linear combination

    of previous nodes More nodes can handle more complexity Training in multiple steps - left to right to evaluate the training set - right to left to propagate errors
  28. Demo: Tensorflow Playground

  29. Deep learning

  30. Deep learning

  31. Convolutional neural networks

  32. Demo: MNIST

  33. Recurrent neural networks

  34. Generative Adversial Nets

  35. State of the art

  36. None
  37. IBM Watson • Healthcare : ◦ Diagnostics ◦ Tests suggestion

    ◦ Prescription recommendations • Legal : ◦ Hired as a lawyer (“Ross”) • Teaching : ◦ Used as a TA (“Jill Watson”) • Cooking : ◦ published a recipe book ◦ new combinations ◦ able to avoid allergies
  38. Google Auto captionning (2014) Two pizzas sitting on top of

    a stove top oven
  39. None
  40. Cloud Vision API Available on Google Cloud Automatic labelling Sentiment

    analysis Text extraction Landmark detection Logo detection Explicit content detection
  41. SyntaxNet and Parsey McParseface

  42. SyntaxNet and Parsey McParseface

  43. SyntaxNet and Parsey McParseface Parsey McParseface can correctly read: •

    The old man the boat. • While the man hunted the deer ran into the woods. • While Anna dressed the baby played in the crib. • Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo. It makes mistakes on: • I convinced her children are noisy. • The coach smiled at the player tossed the frisbee. • The cotton clothes are made up of grows in Mississippi. • James while John had had had had had had had had had had had a better effect on the teacher
  44. Demo: Pride and Prejudice

  45. Google Translate

  46. Google Translate

  47. Google Translate

  48. NeuralArt (Tensorflow) Content Style Output

  49. Facial manipulation (Feb 2015)

  50. Face recognition (April 2014)

  51. Finding similarities in art (Aug 2014)

  52. Audio prediction on video (April 2016)

  53. Predicting scene from sound

  54. Extract instructions from Youtube videos (Nov 2015)

  55. Reading old journals

  56. Creating videos of the future

  57. Applications of NLP at Quora - automatic grammar correction -

    question quality - duplicate question detection - related question suggestion - topic biography quality (= qualifications of writer) - topic labeler (from “science” to narrow topics like “tennis Courts in Mountain View”) - search - answer summaries - automatic answers wiki - hate speech/harassment detection - spam detection - question edit quality
  58. Questions? January 2017