## Teaching

### 2018-2019

**Advanced Machine Learning**, Cycle Master et Master Spécialisé Big Data, Télécom Paris

Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 15 hours of lectures and lab sessions (taught in French).**Certificat d’Études Spécialisées Data Scientist**, Télécom Paris

Introduction to supervised learning, Support Vector Machines. 14 hours of lectures and lab sessions (taught in French).**Machine Learning**, Data Analysis & Decision making, Ecole Centrale de Lille

Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 12 hours of lectures and lab sessions (taught in French).

### 2017-2018

**Advanced Machine Learning**, Cycle Master et Master Spécialisé Big Data, Télécom Paris

Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 15 hours of lectures and lab sessions (taught in French).**Certificat d’Études Spécialisées Data Scientist**, Télécom Paris

Introduction to supervised learning, Support Vector Machines. 10.5 hours of lectures and lab sessions (taught in French).**Machine Learning**, Data Analysis & Decision making, Ecole Centrale de Lille

Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 12 hours of lectures and lab sessions (taught in French).

### 2016-2017

**Advanced Machine Learning**, Cycle Master et Master Spécialisé Big Data, Télécom Paris

Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 18 hours of lectures and lab sessions (taught in French).**Certificat d’Études Spécialisées Data Scientist**, Télécom Paris

Introduction to supervised learning, Support Vector Machines. 14 hours of lectures and lab sessions (taught in French).**Lecture on Learning Similarity and Distance Functions**, Pre-doc Summer School on Learning Systems

Introduction to similarity and distance metric learning at a summer school targeting pre-doc students organized at ETH Zurich. 1.5 hours of lectures (taught in English).**Lecture on Graph Mining**, Allianz France

Introduction to graph mining and machine learning in graphs targeted to Allianz professionals, as part of a series of 7 lectures on data science. 3 hours of lectures (taught in French).

### 2015-2016

**Advanced Machine Learning**, Cycle Master et Master Spécialisé Big Data, Télécom Paris

Advanced machine learning methods : distributed optimization, metric learning, large-scale kernel methods, graph-based learning. 21 hours of lectures and lab sessions (taught in French) and organization of a data challenge with the company Morpho on the topic of rank aggregation for facial recognition.**Certificat d’Études Spécialisées Data Scientist**, Télécom Paris

Introduction to supervised learning, Support Vector Machines. 10 hours of lectures and lab sessions (taught in French).

### 2014-2015

**Advanced Machine Learning**, Cycle Master et Master Spécialisé Big Data, Télécom Paris

Advanced machine learning methods : stochastic approximation, distributed algorithms, structured prediction, large-scale kernel methods. 15 hours of lab sessions (taught in French) and organization of a data challenge with the company Morpho on the topic of metric learning for facial recognition.**Machine Learning and Data Mining**, Cycle Master et Master Spécialisé Big Data, Télécom Paris

Introduction to machine learning and data mining : Python programming, k-nearest neighbors, perceptron, classification and regression trees, large margin and kernel methods, ensemble learning and unsupervised learning. 24 hours of lab sessions, taught in French.

### 2012-2013

**Design and Analysis of Algorithms**, Master Erasmus-Mundus CIMET, University of Saint-Etienne

Introduction to C programming, divide-and-conquer algorithms, dynamic programming, graph algorithms. 22 hours of lab sessions with student projects, taught in English.

### 2011-2012

**Pattern Recognition**, Master Erasmus-Mundus CIMET, University of Saint-Etienne

Introduction to convex optimization, support vector machines (SVM) and their applications. 20 hours of lectures, tutorials and lab sessions, taught in English. In charge of the course unit.**Design and Analysis of Algorithms**, Master Erasmus-Mundus CIMET, University of Saint-Etienne

Introduction to C programming, divide-and-conquer algorithms, dynamic programming, graph algorithms. 22 hours of lab sessions with student projects, taught in English.**Object-Oriented Programming**, Licence professionnelle ATII par alternance, IUT de Saint-Etienne

Introduction to object-oriented programming and Java. 21 hours of lectures, tutorials and lab sessions taught in French to students in cooperative education (work-based learning). In charge of the course.**Introduction to Office Tools**, L1 Sciences Technologie Santé, University of Saint-Etienne

Introduction to information technology : Internet, word processing, HTML, spreadsheet and presentation applications, databases. 12 hours of lab sessions, taught in French.

### 2010-2011

**Design and Analysis of Algorithms**, Master Erasmus-Mundus CIMET, University of Saint-Etienne

Introduction to C programming, divide-and-conquer algorithms, dynamic programming, graph algorithms. 22 hours of lab sessions with student projects, taught in English.