5PMBMLD0 : Introduction to Machine learning and Deep learning - WPMBDAS9

Goal(s)

Introduction to the statistical learning theory and prediction (regression/classification)

• Review of Models/Algorithms for supervised/unsupervised learning
• Illustration de ces algorithmes sur différents jeux de données on different dataset
  (intelligence artificielle, Bioinformatics, vision, etc ...)

Content(s)

• General introduction to the statistical learning theory and prediction (regression/classification)
• Generative approaches: Gaussian discriminant analysis, naïve Bayes hypothesis
• Discriminative approaches: logistic regression
• Prototype approaches: support vector machines (SVM)
• Unsupervised classification (kmeans and mixture model)
• Dictionnary learning / Sparse reconstruction
• Source separation

Basic elements of probability/statistics, filtering