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 2018-2019:
Lecture 2 (Friday 11th January 2019): Exponential GARCH model. Bootstrap methods and their application to model-free prediction and
inference of univariate models. Multivariate models. Change-point models.
Slides
Lecture 3 (Monday 25th February 2019): Long-range dependent and multifractal volatility models.
Wavelet analysis of volatility processes.
Slides
Lecture 4 (Tuesday 26th February 2019): Volatility estimation of high frequency time series.
Deep Learning: convolutional, recurrent and hybrid architectures.
Slides
2009: Aarhus University, PhD lectures, based on the draft version of the book
Large Sample Inference for Long Memory
Processes (2012) Imperial College Press. In 2013, I wrote the review of this book for
Mathematical Reviews®.
If you are a MathSciNet subscriber, you could read this review from my
author profile Additional material not covered in that book:
Slides
2006-2007: Ensae, lectures (in French) on
Long-range dependence and change-points, Applications
to univariate and multivariate financial time series,
The December 2019 issue of the Notices of the American Mathematical Society
contains the paper entitled Machine Learning: Mathematical Theory and Scientific Applications (2019), vol 66,
1813-20, Pdf
The Spring 2020 lectures on deep learning by Yann LeCun at the NYU Center for Data Science.
The lectures, in French, at the Collège de France, by Stéphane Mallat:
The first class requires a good working knowledge of R.
Since the academic year 2016-2017, students undertaking their dissertation on Machine Learning, deep learning, etc., are using
either Python
with the PyTorch,
TensorFlow, and
scikit-learn
libraries, or Torch for R.
The efficiency of the running of Python programs is substantially improved by calling
them within a Julia program.
Some statistical procedures are using Octave, a matrix oriented
programming language for numerical computing, the syntax of which is
similar to Matlab, up to some changes in the syntax of a few functions.
The C++
programming language is still the most powerful language. Valgrind is a useful tool for memory leak debugging,
profiling, etc.
NLopt
is a free/open-source library for nonlinear optimization, callable from C,
C++, Fortran, Python, Julia,
R,
Octave, and Matlab programs.
Donald Knuth's home page. Don Knuth is the author of the celebrated books:
The Art of Computer Programming
You can find there everything important on semi-numerical and numerical algorithms,
TeX etc.
BOINC,
the Berkeley Open Infrastructure for Network Computing, is an open-source software for volunteer and grid computing.
BOINC projects are covering several fields: physics, astronomy,
mathematics, biology, artificial intelligence, cryptography, computer science, etc. If your project is computationally very
intensive, and intellectually attractive, you can transform it into a BOINC project.
Other Teaching and Supervision Resources
Open Math Notes is a repository of freely downloadable mathematical works
in progress hosted by the American Mathematical Society
as a service to researchers, teachers and students.
MathJax is an open source Javascript library that uses
LaTeX
and MathML
for displaying mathematics on webpages, which works on all modern browsers and that I'm
using for my web pages. Further details are provided by the paper published in the Notices of the American Mathematical SocietyMathJax: A Platform for Mathematics on the Web (2012), vol 59, 312-316,
Pdf file.
The Scholarly Open Access website contains a critical analysis of
scholarly open-access publishing, and in particular provides a list of predatory journals and publishers that must be avoided.
Further details are given in J. Beall Predatory publishers are corrupting open access,
Nature 489, 179, (13 September 2012)DOI.
Plagiarism.org:
This organization provides research institutes and academic places
with a database for deterring and detecting plagiarism.