Probability theory and introduction to random processes

Lecturer(s): Elisabeth MIRONESCU, Philippe MICHEL
Course ⋅ 18 hTC ⋅ 14 hAutonomy ⋅ 12 hStudy ⋅ 4 h


Advanced mathematics for mathematical enginneering with a focus on measure theory, probability theory. This course is a pre-requisit for stochastic processes, machine learning, mathematical finance or biomathematics. The remaining of the course concerns the bases of functional analysis and a glimpse of partial differential equations.

Palabras clave

measure theory, integrals, topology, functional analysis, probability theory, partial differential equations


  1. Measure theory, integrals, probability theory
  2. Topology, functional analysis, introduction to partial differential equations

Learning Outcomes

  • understanding and proof mastering of analysis and probability
  • using an appropriate tehoretical framework when dealing with complex problems
  • giving examples and counter-examples to illustraite theoretical mathematical notions


Final mark = 75% Knowledge + 25% Know-how Knowledge mark = 100% final exam Know-how mark = 100% continuous assessment