• Applied modeling to complex networks (practical tutorials)

    • Fundamentals of Network Science, e.g., classic random models, centralities, small-world phenomenon, etc...
    • Focus classes on advances topics, e.g., dynamic networks, graph algorithmic, community detection, machine learning on graphs
    • An introduction to cutting-edge research topics, such as Graph embedding and Graph Convolutional Neural Networks (GCN).
  • Random Signal Processing (practical tutorials)

    • Random signals
    • Spectral estimation
    • Quadratic detection
    • Linear prediction
  • Linear and bilinear algebra, matrix analysis (practical tutorials)

    • Quadratic forms
    • Endomorphism in Euclidean space
    • Endomorphism in Hermitian space
    • Linear systems

  • Geometric algebra (practical tutorials and written examinations)

    • Inner product
    • Orthogonality
    • Orthogonal projection on finite-dimensional subspaces
    • Affine hyperplane in Euclidean spaces
    • Vectorial isometry in Euclidean spaces
    • Vectorial endomorphism in Euclidean spaces

  • Introduction to numerical analysis (practical tutorials and written examinations)

    • Polynomial interpolation
    • Quadrature method
    • Root-finding algorithms
    • Numerical methods for differential equations

Cycles Universitaires Préparatoires aux Grandes Ecoles

Intensive preparation for the entrance examinations to selective higher education establishments

  • Fundamental of Mathematics (oral examinations)

    • Complex numbers
    • Sequences and limits
    • Real-valued functions of a real variable
    • Limits and continuity
    • Derivation of real-valued functions
    • Integer arithmetic
    • Polynomials

  • Basic mathematical techniques (lectures, practical tutorials and written examinations)

    • Riemann integration
    • First and second order linear differential equations
    • Complex numbers
    • Vector spaces
    • Geometry in the plane and in space