Theory of Machine Learning

• There are two types of tasks:

  • Supervised
  • Unsupervised

• Supervised : We have prior knowledge of what the values of our sample should be. The data is usually labelled. Supervised learning is typically done in the context of classification, when we want to map input to output labels, or regression, when we want to map input to a continuous output. Common algorithms of supervised learning are :

  • Linear regression :

  • KNN (K Nearest Neighbours)

  • Naive Bayes :

  • Support Vector Machine :

  • Random Forest :

  • Artificial Neural Network :

• Unsupervised : The data is not labelled or it has unlabelled data. So its goal is to infer the natural structure present within a set of data points. The most common tasks within usupervised learning are clustering, representation learning, and density estimation. Some most common algorithms include :

  • K-means Clustering :

  • principal component analysis :

  • Autoencoders :