I have taught at every university level since 1999, to students from mathematics,
statistics, medicine and biology. Below are the main courses I have designed or
coordinated, and the material I make freely available online.
The Mathematical Engineering of Deep Learning
An intensive unit of eight chapters on the mathematical foundations of deep learning,
designed with two colleagues for the AMSI Summer School — the largest mathematics
event in Australia for honours and postgraduate students. The material is freely
available, and is also published as a book by Chapman & Hall/CRC.
deeplearningmath.org
Introduction to Machine Learning and Deep Learning
A workshop designed for the Faculty of Health and Medical Sciences at the
University of Copenhagen, delivered in 2025 and 2026. It introduces the core ideas
of machine learning and deep learning to an audience of health and medical
researchers. Slides and code are on GitHub.
github.com/benoit-liquet/MLDL
Since 2015 — Professor
Anglet and Pau campuses
Master level, as convenor and instructor:
Mathematical Engineering of Deep Learning (MSID International and
Mathematics, Modeling and Simulation),
Advanced Machine Learning (M2 MSID and M2 Big Data),
Machine Learning (M1 Big Data),
Advanced Data Mining and Data Mining, including neural networks
and deep learning (M2 MSID),
Multivariate Data Analysis (M1 MSID),
Univariate and Multivariate Statistics (M1 Erasmus Mundus),
Applied Statistics for Environmental Data and
Spatio-temporal Data Analysis (M2 DynEA),
Time Series Analysis (M2 DynEA and M2 QUAMA),
Generalized Linear Models (M2 DynEA).
Undergraduate level, as convenor and instructor:
Probability and Statistics (L1),
Biostatistics 1 and Data Analysis (L2),
Biostatistics 2 (Biology of Organisms, lectures and practicals).
2020 – 2024 — Professor
Sydney, Australia
Designer and instructor of two new Master-level courses:
Statistical Learning (STAT8107, 2023) and
Bayesian Data Analysis (STAT8150, 2021).
Convenor and instructor of Modern Computational Statistics Methods
(STAT8830, 2021–2023), Multivariate Analysis (STAT8121, 2021–2022) and
Mathematical Background for Biostatistics (STAT8602, 2020, for the
Biostatistics Collaboration of Australia). Instructor of
Generalized Linear Models (STAT8111, 2020–2023) and
Graphics, Multivariate Methods and Data Mining (STAT6102, 2021–2022).
2013 – 2015 — Senior Lecturer
Brisbane, Australia
Co-designer with Dirk Kroese of the new course Advanced Analysis of Scientific
Data (STAT1301). Convenor of Analysis of Scientific Data (STAT1201,
900 undergraduate students) and Experimental Design (STAT3003). Instructor
of Advanced Statistics (STAT4401). Supervision of honours and Masters students.
January – February 2021
AMSI Summer School, Australia
Designer and instructor, with two colleagues, of the new course
The Mathematical Engineering of Deep Learning at the Australian
Mathematical Sciences Institute summer school. Developed the intensive unit of
eight chapters that became the basis of the book.
2013 – 2014 and 2017 – 2018
Universitas Sebelas Maret, Indonesia
Broadening teaching activities through collaborations between Sebelas Maret and
both UPPA and the School of Mathematics and Physics at the University of
Queensland. Designer of a sequence of lectures on R, convenor and instructor of
Advanced Analysis of Scientific Data (STAT1301), and developer of the
web teaching support for these courses (forum, recordings).
Earlier positions (1999 – 2011)
2007 – 2011 — Associate Professor
Bordeaux 2 University, ISPED (192 h)
Convenor of Modelling, Introduction to Statistics,
Inferential Statistics, Introduction to Modelling (on campus and
e-learning) and Statistical Methods in Health (online) for the Master of
Public Health. Convenor of the Introduction to R course at the ISPED
summer school. Instructor in R, Algorithms and Numerical Methods,
Multidimensional Data Analysis and Data Analysis for the
Biostatistics stream. Supervision of first- and second-year Master trainees.
2004 – 2007 — Associate Professor
University of Grenoble, IUT STID (192 h)
Undergraduate teaching in inferential statistics, analysis, probability, algebra,
ANOVA and linear regression — lectures, tutorials and practicals with SAS, SPSS,
Minitab and R. Teaching Statistics, Combinatorics and Computing in the
Master of Statistics at Université Pierre Mendès France, and supervision of
Master students in Grenoble.
2003 – 2004 — Lecturer
Montpellier 2 University (96 h)
Inferential statistics for mathematics students; statistics for the MASS Master;
discriminant analysis and scoring.
2002 – 2003 — Lecturer
Bordeaux 1 University (96 h)
Analysis and algebra for mathematics students; probability and statistics in the
Mathematics Master; practical courses in the MSRO Master (stochastic modelling
and operational research).
1999 – 2002 — Lecturer and Assistant Lecturer
Bordeaux 2 University
Statistics for first-year medical students; probability, algebra, statistics and
computing for biology students; computer science for medical and sport students.