Courses

Teaching topics

2024

Master DataAI	“Formal languages” (in TPT-DATAAI900) 3 hours, Télécom Paris Introduction, Formal grammars, Regular expressions
Master DataAI	“Knowledge Base Construction” (TPT-DATAAI964) 21 hours, Télécom Paris Web page
Master DataAI	“Softskills seminar” (TPT-DATAAI941) 21 hours, Télécom Paris Web page
MS Smart Mobility	“Natural Language Processing” (B7) 14 hours, Télécom Paris Web page
MS Big Data	“LLMs and society” (in BGD709) 1.5 hours, Télécom Paris Web page
MS Big Data	“Symbolic NLP” (in BGD709) 6 hours, Télécom Paris Web page
MS Big Data	“Data Security” (in BGD709) 6 hours, Télécom Paris Web page
Executive Education	“Natural Language Processing” (FC9BD10) 12 hours, Télécom Paris (Espace Magnetik) Web page
CES-IA-2023-09	“Information Extraction” (FL9BD03-11) 6 hours, Télécom Paris (Espace Magnetik) Web page
Engineering Program	“Information Extraction” (IA327) 3 hours, Télécom Paris Web page
Master DataAI	“Data Security” (in TPT-DATAAI951) 6 hours, Télécom Paris Web page

2023

Executive Education	“Natural Language Processing” (FC9BD10) 8 hours, Télécom Paris/Magnetik Web page
Master DataAI	“Knowledge Base Construction” (TPT-DATAAI964) 21 hours, Télécom Paris Web page
Master AI	“Natural Language Processing” (INF632) 12 hours, Ecole polytechnique Moodle
Master DataAI	“Softskills seminar” (TPT-DATAAI941) 21 hours, Télécom Paris Web page
MS Big Data	“Data Security” (in BGD709) 6 hours, Télécom Paris Moodle
MS Big Data	“Information Extraction” (in BGD709) 9 hours, Télécom Paris Moodle
Master DataAI	“Data Security” (in TPT-DATAAI951) 6 hours, Télécom Paris Web page
MS Smart Mobility	“Information Extraction” (B7.3) 6 hours, Télécom Paris Web page
CES-DS-2022-11	“Information Extraction” (M3, FC9BD07) 15 hours, Télécom Paris (Espace Formeret Vinci) Web page

2022

Executive Education	“Introduction to NLP” (FC9BD10 ) 7 hours, Télécom Paris Web site
Master DataAI	“Softskills seminar” (TPT-DATAAI941) 21 hours, Télécom Paris Web page
Master DataAI	“Formal languages” (in TPT-DATAAI900) 3 hours, Télécom Paris Introduction, Formal grammars, Regular expressions
Master DataAI	“Knowledge Base Construction” (TPT-DATAAI964) 21 hours, Télécom Paris Web page
Master AI	“Natural Language Processing” (INF632) 12 hours, Ecole polytechnique Introduction, Web page
Summer School	“Neuro-symbolic Methods for Fact Prediction” (in AIB-3) 3 hours, AI summer school in Bergen/Norway Slides
MS Big Data	“Fact Extraction” (in INF344) 9 hours, Télécom Paris Moodle
NoRDF Seminar	“Knowledge Representation and Fact Extraction” (NoRDF01) 2 hours, Télécom Paris Web page
Engineering program	“Knowledge Representation” (in SD213) 3 hours, Télécom Paris Introduction, Knowledge Representation, Fact Extraction, Knowledge Bases in Industry, Knowledge Bases in Research

2021

Master DataAI	“Data Security” (in TPT-DATAAI951) 6 hours, Télécom Paris Web page
Master DataAI	“Softskills seminar” (TPT-DATAAI941) 21 hours, Télécom Paris
Master AI	“Natural Language Processing” (INF632) 12 hours, Ecole polytechnique Web page
Master DataAI	“Formal languages” (in TPT-DATAAI900) 3 hours, Télécom Paris Web page of the course, Slides on formal grammars, Slides on regular expressions
Master DataAI	“Knowledge Base Construction” (TPT-DATAAI964) 21 hours, Télécom Paris
Engineering program	“Knowledge Representation” (in SD2013) 3 hours, Télécom Paris Knowledge Representation, Fact Extraction, Knowledge Bases in Industry, Knowledge Bases in Research
CES-DS-2021-04	“Information Extraction” (M3, FC9BD07) 10 hours, Télécom Paris (Villa Thoréton) Web page
MS Smart Mobility	“Information Extraction” (in C3) 6 hours, Télécom Paris Web page

2020

Master DataAI	“Knowledge Base Construction” (TPT-DATAAI964) 21 hours, Télécom Paris
Master DataAI	“Data Security” (in TPT-DATAAI951) 6 hours, Télécom Paris Web page
Master AI	“Natural Language and Speech Processing” (INF632) 12 hours, Ecole polytechnique Web page
Master DataAI	“Softskills seminar” (TPT-DATAAI941) 21 hours, Télécom Paris Web page
Summer School	“Knowledge Bases” 6 hours, DaSE Summer School at East China Normal University (remotely) Web page
CES-DS-2020-05	“Information Extraction” (M3, FC9BD07) 10 hours, Télécom Paris Web page
Engineering program	“Knowledge Representation” (in SD2013) 1.5 hours, Télécom Paris Slides: Knowledge Representation, Slides: Knowledge bases, Slides: Research
MS Big Data	“Data Security” (in INF344) 6 hours, Télécom Paris
MS Smart Mobility	“Information Extraction” (in Introduction to Data Mining) 6 hours, Télécom Paris Web page

2019

CES-DS-2019-10	“Information Extraction” (M3, FC9BD07) 10 hours, Télécom Paris
M2 Data+Knowledge	“New Data on the Web” (DK906) 15 hours, Télécom Paris
M2 Data+Knowledge	“Introduction to Research and Business” (in DK915) 6 hours, Paris-Saclay Web page, Slides: How to give good talks, Slides: How to do a PhD
M2 Data+Knowledge	“Softskills seminar” (DK907) 21 hours, Télécom Paris Web page
Master AI	“Natural Language and Speech Processing” (in INF632) 12 hours, Ecole Polytechnique Web page
PESTO-IA	“Semantic Web” (SW) 6 hours, Télécom Paris
Summer School	“Rule Mining” (C2) 3 hours, Reasoning Web Summer school, Bolzano/Italy
Engineering program	“Knowledge Representation” (in SD213) 1.5 hours, Télécom ParisTech Knowledge Representation, Knowledge Bases, Semantic Web
CES-DS-2019-01	“Information Extraction” (M3) 10 hours, Télécom ParisTech
MS Smart Mobility	“Information Extraction” (in Introduction to Data Mining) 6 hours, Télécom ParisTech

2018

M2 Data+Knowledge	“Softskills seminar” (DK907) 21 hours, Télécom ParisTech Web page
M2 Data+Knowledge	“New Data on the Web” (DK906) 15 hours, Télécom ParisTech Web page
Master AI	“Natural Language and Speech Processing” (in INF632) 12 hours, Ecole Polytechnique Web page
M2 Data+Knowledge	“Introduction to Research and Business” (in DK915) 6 hours, Télécom ParisTech Web page, Slides: How to give good talks, Slides: How to do a PhD
CES-DS-2018-05	“Information Extraction” (M3) 10 hours, Télécom ParisTech Web page
MS Big Data	“Web Data” (in INF344) 20 hours, Télécom ParisTech
MS Smart Mobility	“Information Extraction” (in Introduction to Data Mining) 6 hours, Télécom ParisTech

2017

M2 Data+Knowledge	“Knowledge Base Construction” (DK906) 21 hours, Télécom ParisTech
ATHENS	“Information Extraction” (TPT393) 30 hours, Télécom ParisTech
M2 Data+Knowledge	“Softskills seminar” (DK907) 15 hours, Télécom ParisTech
CES-DS-2017	“Information Extraction” (M3) 10 hours, Télécom ParisTech
M2 Data+Knowledge	“How to write a PhD thesis” (in DK915) 1 hours, Paris-Sud
MS Big Data	“Information Extraction” (in INF344) 6 hours, Télécom ParisTech
Erasmus+	“Semantic Web” 6 hours, Universitatea Politehnica din Bucuresti

2016

M2 Data+Knowledge	“Softskills seminar” (DK907) 15 hours, Télécom ParisTech
M2 Data+Knowledge	“Knowledge Base Construction” (DK906) 21 hours, Télécom ParisTech
Leopoldina	“Information Extraction” 0.75 hours, Saarland University
ATHENS	“Information Extraction” (TPT393) 30 hours, Télécom ParisTech
CES Data Scientist	“Information Extraction” (M3) 11.25 hours, Télécom ParisTech
CES Data Scientist	“Rule Mining” 1.5 hours, Télécom ParisTech
Summer School	“Information Extraction” 3 hours, RuSSIR summer school at Saratov University
MS Big Data	“Information Extraction” (in INF344) 12 hours, Télécom ParisTech
CES Data Scientist	“Information Extraction” 7.5 hours, Télécom ParisTech

2015

M2 Data+Knowledge	“Knowledge Base Construction” (DK906) 21 hours, Télécom ParisTech
CES Data Scientist	“PageRank” 3 hours, Télécom ParisTech
ATHENS	“Information Extraction” (TPT393) 30 hours, Télécom ParisTech
M2 Data+Knowledge	“Softskills seminar” (DK907) 15 hours, Télécom ParisTech
MS Big Data	“Information Extraction” (in INF344) 12 hours, Télécom ParisTech
CES Data Scientist	“Information Extraction” 7.5 hours, Télécom ParisTech

2014

Engineering program	“Ontologies and Description Logics” (in INF221) 1.5 hours, Télécom ParisTech
Master New Tech	“Technologies du Web” 21 hours, Télécom ParisTech
Engineering program	“Rule Mining” (in INF230) 1.5 hours, Télécom ParisTech
ATHENS	“Information Extraction” (TPT393) 22 hours, Télécom ParisTech
German Habilitation	“An Introduction to NLP” 0.75 hours, Télécom ParisTech
CES Data Scientist	“Information Extraction” 7.5 hours, Télécom ParisTech
MS Big Data	“Information Extraction” (in INF344) 9 hours, Télécom ParisTech
Engineering program	“NLP and the Semantic Web” (in INF348) 6 hours, Télécom ParisTech
ATHENS	“Information Extraction” (TPT393) 22 hours, Télécom ParisTech
Engineering program	“Technologies coté Serveur” (in INF228) 1.5 hours, Télécom ParisTech
Engineering program	“Ontologies and Description Logics” (in INF221) 1.5 hours, Télécom ParisTech

2013

Master New Tech	“Technologies du Web” 21 hours, Télécom ParisTech
Master CS	“Information Extraction and the Semantic Web” 22 hours, Saarland University

2012

Engineering program

“Semantic Web”
3 hours, Télécom ParisTech

2011

Pro bono	“Information Extraction” 4.5+1.5 hours, ESP, UCAD , UGB / Senegal
Pro bono	“Semantic Web” 3+1.5 hours, UCAD , UGB / Senegal
Pro bono	“Knowledge Representation” 2 hours, ESP, Dakar, Senegal
Pro bono	“Natural Language Processing” 6 hours, GBU, Saint Louis / Senegal
Engineering program	“Natural Language Processing” 6 hours, Télécom ParisTech
Engineering program	“Information Extraction” (in INF347) 4.5+1.5 hours, Télécom ParisTech
Engineering program	“Semantic Web” (in INF347) 3+1.5 hours, Télécom ParisTech
Engineering program	“Knowledge Representation” (in SD2013) 3 hours, Télécom ParisTech
ATHENS	“Information Extraction” (in TPT393) 3+3 hours, Télécom ParisTech

2010

PESTO	“Natural Language Processing” 2 hours, Télécom ParisTech
PESTO	“Information Extraction” 4 hours, Télécom ParisTech
PESTO	“Semantic Web” 4 hours, Télécom ParisTech
Master New Tech	“Information Extraction, the Semantic Web and all that” 3 hours, Télécom ParisTech

2003

free course

“Introduction to Java”
12 hours, Université Pierre Mendès France
Plan