Sem Spirit

Machine Learning

Machine learning is the field of artificial intelligence that aim to provide computers with the ability to learn without being explicitly programmed. There are three categories of machine learning methods :

  • Supervised learning : The machine is trained on a subset of the dataset containing example inputs associated to their desired outputs. The goal is to find a correlation that maps inputs to outputs. The more the dataset is structured and labelled, the best is the model quality.
  • Unsupervised learning : No labels are given to the machine, leaving it on its own to find structure in its input. The goal is to find some hidden patterns or correlations within the dataset.
  • Reinforcement learning : The machine evolves in a dynamic environment in which it must solve a specific problem (driving a car, playing a game). The machine gets rewards and punishments while processing the data read from the environment interactions.

Basically, the steps for building a model in machine learning are :

  • (1) Analysing the data and chosing the learning strategy
  • (2) Preparing the data
  • (3) Applying the learning strategy on the training set
  • (4) Evaluating the model prediction accuracy on the test set
  • (5) Improvement Loop : Correcting/adjusting the parameters of the learning algorithm in step (2) in order to improve the results in step (3) until the model is working properly.