Textbook | Current courses | OpenClassrooms | Continuing education | Older courses

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Textbook

[In French] Introduction au Machine Learning. See the [book's webpage].

2021 – 2022 courses

Winter 2022: Large scale machine learning (PSL week)
A week-long course on large-scale machine learning, co-organized with Fabien Moutarde. [course website].

Winter 2022: Data, Images, Models and Learning
[In French] Lecturer on the topics of "graphs, machine learning and health" (4.5 hours) and "data integration with machine learning" (1.5 hour) for students of the "Data, Images, Models and Learning" research term organized by Étienne Decencière and David Ryckelynck. [course website].

January 2022: Foundations of machine learning
[In French] A 3-day course for the AI Project Manager Certificate (continuing education). [course github].

Fall 2021: Machine learning, chemistry, and therapeutic research
1 hour for students of the Chemistry & Life Sciences Master's degree of PSL. [slides].

Fall 2021: Machine learning (2A/3A)
[In French] A one-week introduction to machine learning (masters level), co-organized with Fabien Moutarde.

Fall-Winter 2021-22: Probabilities
[In French] TAing for the upper-undergraduate probabilities course, which is a subsection of the Integral, Differential and Stochastic calculus course. [course website].

Fall 2021: Machine learning (STePE)
[In French] An introduction to machine learning for the STePE-PSL Master. [Syllabus (pdf)].

Older courses

Introductions to machine learning

I have been in charge of a number of courses introducing machine learning to different audiences.

Graduate level, in computer science / mathematics

Graduate level, in geosciences

  • [In French] 30 hours for master students in geosciences, in the M2 STePE-PSL masters, in which I'm giving 12 hours of lecture (Fall 2020).

For undergraduate lecturers in computer science / mathematics

  • [In French] A day-long course for undergraduate lecturers (in classes préparatoires), through the LIESSE society (Spring 2019, Spring 2021). Course website

High-school students

Continuing education
I have taught various continuing education courses in machine learning, including at Institut Curie or (from 2018 to 2020) with CentraleSupelec Executive Education or for individual companies. Over the course of one or two days, I propose to introduce participants to various machine learning concepts, in French or in English. The courses can include practical sessions in Python. For examples of practical sessions, see my machine learning notebooks on github.

Online courses with OpenClassrooms
[In French] I have contributed to setting up two paths: Data Scientist an Machine Learning Engineer in collaboration with CentraleSupélec. I have also designed, written and shot four courses:

Other courses

Other courses of which I have been in charge.

Data Science (Spring 2020, Spring 2021)
[In French] A 30-hour course introducing data science to Mines ParisTech students. Course website (2021).

PSL Intensive Week on ML & Genomics (Spring 2021)
A week-long course on machine learning for genomics, co-organized with Anaïs Baudot, Camille Berthelot, Laura Cantini, Vivien Goepp, Flora Jay and Paul Villoutreix. Course website.
Materials for the day on feature selection and genome-wide association studies.

Large Scale Machine Learning (Spring 2017, Spring 2018, Spring 2019, Spring 2021, Spring 2022)
A week-long course on large-scale machine learning, co-organized with Fabien Moutarde at Mines ParisTech. Course websites: [2022], [2021] , [2019], [2018] , [2017]

Introduction to genomics and bioinformatics (Fall 2014, Fall 2015, Fall 2016, Fall 2017, Fall 2018, Fall 2019)
[In French] A 40-hour graduate course at Mines ParisTech, also open to students of the MSc in Chemistry & Life Sciences of PSL from 2017 to 2019, co-organized with Véronique Stoven and Thomas Walter in 2014 and with Thomas Walter from 2015 to 2019.

Thematic schools

Lectures given at thematic schools targeted to graduate students and researchers.

Digicosme Thematic School 2021 on Graph as models in life sciences: Machine learning and integrative approaches (online)
Boosting genome-wide association studies (GWAS) with biological networks [slides]

Inserm Workshop 266: Introduction to Machine Learning, from Biology to Health (online)
Introduction to machine learning [slides]

4th course on Computational Systems Biology of Cancer: Multi-omics and Machine Learning Approaches (online)
Machine learning techniques for multi-modal data analysis.

2021 Summer School on Machine Learning Frontiers in Precision Medicine (online)
Machine learning techniques for data integration [slides] [video]

2021 CIMPA Research School on Machine learning Approaches - Application to health data (online)
[In French] High dimensional machine learning, sparsity and health
[In French] Machine learning, graphs and health [videos & quizzes]

EMBO FEBS 2020 Lecture Course on Cancer systems biology: Promises of artificial intelligence (online)
Feature selection with regularization in high-dimensional genomic data.

Networks and molecular biology research school (2020) CIRM (Marseille, France)
Network-guided feature selection in high-dimensional genomic data [slides].

BioComp Summer School 2017 Roscoff, France
Around Machine Learning in 90 Minutes [slides]
Interpretable Machine Learning for Precision Medicine [slides].

The 2017 Microbiome Summer School (Université Laval, Québec, Canada)
Machine learning for efficient biomarker discovery [slides] [tutorial].

Invited lectures

Lectures given in courses organized by others.

Machine learning, chemistry, and therapeutic research

  • [In French] For students of UE Industrie 4.0 at Chimie ParisTech (Winter 2021)
  • For students of the Chemistry & Life Sciences Master's degree of PSL, as part of lecture taught with Thomas Walter (Fall 2017, Fall 2018, Fall 2020, Fall 2021) [slides (2021)]
  • [In French] 3 hours for students of the "Data, Images, Models and Learning" research term at Mines ParisTech, organized by Étienne Decencière and David Ryckelynck (Winter 2021) [course website]

High-dimensional feature selection and biomarker discovery

Machine learning and health

  • [In French] 1-hour lecture for students of the MSIT Master at MINES ParisTech (Spring 2019) [slides].
  • [In French] 2 x 3 hours for students of the "Engineering & Health" research term at Mines ParisTech, organized by Laurent Corté and Yannick Tillier (Fall 2020)

Graph data mining and applications to chemoinformatics (Winter 2015, Winter 2016, Fall 2016)

  • A day-long course for students of the M2BI MSc. at Paris Diderot.

Support vector machines, graph kernels, & bioinformatics (Spring 2015, Spring 2016, Fall 2017, Fall 2019)

Data Mining in Bioinformatics (Winter 2012-13, Winter 2011-12)
Course (M.Sc. level) taught with Karsten Borgwardt and Dominik Grimm at the University of Tübingen.

  • Frequent subgraph mining;
  • Clustering in bioinformatics: clustering gene expression data;
  • Graph mining for chemoinformatics and drug discovery.

As teaching assistant

Labs and practical sessions in courses organized by others. Probabilities (Spring 2019, Fall-Winter 2019-20, Fall-Winter 2020-21)
TAing for the upper-undergraduate probabilities course at Mines ParisTech, which is a subsection of the Integral, Differential and Stochastic calculus course. See the latest version of the course material. Course taught by Michel Schmitt (Spring 2019) then Émilie Chautru and Thomas Romary.

Numerical tools for mathematics (Fall-Winter 2019-20)
TAing for some sessions (namely, numerical python) the upper-undergraduate numerical tools course at Mines ParisTech (see course documentation).

Bioinformatics seminar (Spring 2012, Spring 2013)
Course taught by Karsten Borgwardt at Eberhard Karls Universität Tübingen (Germany)

2006—2008 Introduction to Probabilities and Statistics for Computer Science (Summer 2008)
UC Irvine, Irvine, CA (United States)

Introduction to Artificial Intelligence (Summer 2006)
Course taught by Seyoung Kim at UC Irvine, Irvine, CA (United States)