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Machine learning - AI

Learn more about machine learning and AI.

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Machine learning - AI

  1. 1. Machine learning presentation
  2. 2. 2 When a solution to a problem can only be modelized by the data that defines it, you should use machine learning to reach that solution. There exist many different machine learning algorithms , but they often fall into one of those categories : o Supervised learning o Unsupervised learning o Semi supervised learning o Reinforcement learning What is machine learning ? Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
  3. 3. Supervised Learning
  4. 4. 4 o In supervised learning all the data is labeled o Mostly used to learn patterns in data in order to generate predictions o A loss function can be computed based on true labels Supervised learning Learning with fully labeled data Image Source :
  5. 5. 5 Mostly used on regressions and classifications : o Price predictions o Age prediction o Gender classification o Spam detection o Medical diagnosis Supervised learning applications Image Source :
  6. 6. Unsupervised Learning
  7. 7. 7 o The data is not labeled o The network must find patterns and similarities in the data o Unsupervised learning is widely used for clustering and to generate data visualizations Unsupervised learning Finding similarities between unlabelled data Image Source : applications/main-qimg-e510d5175c56d0b7d78e8c59a7a8c8d5/
  8. 8. 8 o Clustering similar images together o Market segmentation o Data visualization generation o Association problems o Dimensionality reduction Unsupervised learning applications Clustering , visualization and association problems Image Source :
  9. 9. Semi Supervised Learning
  10. 10. 10 Getting a big labeled dataset is no east task, Semi supervised learning allows us to work with mostly non labeled data. o The data is mostly non labeled o Two pass algorithm to combine the strenght of unsupervised and supervised learning 1. Run an unsupervised machine learning algorithms to cluster the data based on the labeled data 2. Easily assign labels based on the clusters 3. Run a supervised machine learning algorithm with the newly created labels Semi supervised learning Merging both supervised and unsupervised learning to improve performances
  11. 11. Reinforcement Learning
  12. 12. 12 o Experience driven learning o Closer to human learning mechanism o Sparse delayed feedback o Value network : defines the win situation o Policy network : defines the actions to take in order to win o Behavioral learning Reinforcement learning A radically different approach Image Source :
  13. 13. 13 o AlphaGo o Video games AI o Autonomous car behavior o Autonomous drones / swarm behavior Reinforcement learning applications Capable of complex real time decisions
  14. 14. 14 Summary Image Source :
  15. 15. 15 Perceptron
  16. 16. 16 Multi layer perceptron Image Source :
  17. 17. 17 Auto encoder Image Source :
  18. 18. 18 Convolution Image Source :
  19. 19. 19 Convolutive neural network Image Source :
  20. 20. 20 Generative adversarial network Image Source :