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UE Machine learning fundamentals

User information

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Degrees incorporating this pedagocial element :

Description

  • Consistency of the Empirical Risk Minimization
  • Uniform Generalization Bounds and Structural Risk Minimization
  • Unconstrained Convex Optimization
  • Binary Classification algorithms (Perceptron, Adaboost, Logistic Regression, SVM) and their link with the ERM and the SRM principles
  • Multiclass classification
  • Application and experimentations

Evaluation : Homeworks (30%), Final exam (70%)

Recommended prerequisite

Statistics and probability (BSc)

Targeted skills

Understanding of fundamental notions in Machine Learning (inference, ERM and SRM principles, generalization bounds, classical learning models, unsupervised learning, semi-supervised learning.