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Lectures & Math Resources

A Few Math Resources

Online Lectures Material

The list of recommended machine learning (ML) and computing science (CS) resources for the lectures and the dissertations are respectively here (ML) and here (CS).

  • 2010-: Institut Mines-Télécom Atlantique, Brest, lectures (in French) on nonstationary processes (Mathematical and Computational Engineering Lectures).
    My 3rd year lectures on nonstationary processes for the academic year 2020-2021 will take place in March 2021. They have been quite rewritten from scratch, after reviewing a long series of papers and books (over 70 and counting) on these issues for Mathematical Reviews®. The new section on deep learning and AI is obviously expanding. The writing of the book supporting these lectures is in progress.
    List of past lectures for the academic year 2020-2021:
    • Lecture 1 (Monday 8th March 2021): Volatility models. GARCH model: definition, properties, dependence structure, QML estimation.
    • Lecture 2 (Tuesday 9th March 2021): Exponential GARCH model. Bootstrap methods and their application to model-free prediction and inference of univariate models. Multivariate models. Change-point models.
    • Lecture 3 (Monday 22th March 2021): Long-range dependent and multifractal volatility models. Wavelet analysis of volatility processes.
    • Lecture 4 (Tuesday 23th March2021): Volatility estimation of high frequency time series. Deep Learning: convolutional, recurrent and hybrid architectures, with the PyTorch and libraries.
Updated January 2, 2021.