Machine learning

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Case study notes[1]


Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks.[1] It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.[2]


  1. Behavioural data
  2. Cloud delivery models:
    1. Infrastructure as a service (IaaS)
    2. Platform as a service (PaaS)
    3. Software as a service (SaaS)
  3. Cloud deployment models
  4. Collaborative filtering
  5. Content-based filtering
  6. Cost function
  7. F-measure
  8. Hyperparameter
  9. K-nearest neighbour (k-NN) algorithm
  10. Matrix factorization
  11. Mean absolute error (MAE)
  12. Overfitting
  13. Popularity bias
  14. Precision
  15. Recall
  16. Reinforcement learning
  17. Right to anonymity
  18. Right to privacy
  19. Root-mean-square error (RMSE)
  20. Stochastic gradient descent
  21. Training data


An excellent, and I truly mean excellent example is MarI/O, a machine learning program that learns how to play mario, and mario kart.

  1. Super Mario World:
  2. Mario Kart: