الخطوط العريضة للقسم

►Lecture 2: Uncertainty and imprecision in machine learning: learning, decision and evaluation
◄Lecture 4: Introduction to notions of calibrated and valid predictions

  • Dates: 25/11 (14h15 - 18h30), Vu-Linh Nguyen

    This lecture will provide some first illustration as to how the mathematical elements of the previous lectures can be used to build some simple imprecise classifiers.  Simple illustrations and examples will be provided.

    Objectives of the lecture:

    After this lecture students should be able to

    • Use IDM and related models in Naïve (credal) classifier (NCC)
    • Use IDM and related models in decision trees
►Lecture 2: Uncertainty and imprecision in machine learning: learning, decision and evaluation
◄Lecture 4: Introduction to notions of calibrated and valid predictions