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14) Discrete inference and learning course (MVA, ENS Paris-Saclay and Centrale Supelec, France), 2017 - present.

Lecture 6: Standard learning. (pdf)

Lecture 7: Modern learning. (pdf)

Lecture 8: Recommender systems. (pdf)

Lecture 9: Final remarks. (pdf)


13) Tutorial on Machine learning in remote sensing: best practice and recent developments (IGARSS conference, TX, USA), 2017. (slides)


12) Discrete optimisation course (Centrale Supelec, France), 12h of course + 6h of practical sessions, 2017 - present.


11) Mathematical methods course (Master Data Science & BA, Centrale Supelec, France), 25h, 2016 - present.

Lecture 1: Introduction. (pdf1)

Lectures 2-5: Linear Algebra. (pdf2 pdf3 pdf4 pdf5)

Lecture 6: Integral Transforms. (pdf6 Laplace Table)

Lecture 7-8: Ordinary Differential Equations. (pdf7 pdf8)

Lecture 9: Partial Differential Equations. (pdf9)

Lecture 10: Complex Numbers and Variables. (pdf10)

Lecture 11: Shape of Data. (pdf11)

Homework 1. (pdf-homework1)


10) Spaceborne sensors and their applications course (Master PPMD, ENSG, France), 3h, 2013 (pdf).

9) Digital Imaging course (Master ISAB, University of Nice, France), 2h of course + 2h of practical sessions, 2012 - present.

8) Practical Sessions on Matlab (Master ISAB, University of Nice, France), 10h, 2012 - present.

7) Computer Vision course (TNTU, Ukraine), 8h, 2012.

6) Methods for Statistical Data Analysis (SI3, 1st year, University of Nice Sophia Antipolis, France), 39h, 2012.

5) Image Processing course (Traitement d'Images, ASI, 2ème année, Grenoble INP - ENSI3, France), 12h, 2010.

Download all transparencies

Final exam


4) Matlab for Image Processing (continuing education) (Grenoble INP, France), 7 hours x 3 = 21 hours, December 2009 - January 2010.


3) Pattern Recognition course (University of Iceland, Iceland), September - November 2009.

Lecture 1: Introduction. (pdf)

Lecture 2: Mathematical Preliminaries. (pdf)

Lecture 3: Bayesian Decision Theory. (pdf)

Lecture 4: Maximum Likelihood Estimation. (pdf)

Lecture 5: Non-Parametric Classification. (pdf)

Lecture 6: Linear Discriminant Functions. (pdf)

Lecture 7: Feature Extraction for Representation and Classification. (pdf)

Lecture 8: Unsupervised Analysis. (pdf)


2) E-learning course of Mathematical Morphology (in the frame of Hyper-I-Net project), 2h, July 2009:

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Part 1: Basic concepts of mathematical morphology. (pdf) (wmv)

Part 2: Mathematical morphology for grey-scale and hyperspectral images. (pdf) (wmv)

Part 3: Remote sensing application 1: Classification of hyperspectral images of an urban area using morphological profiles. (pdf) (wmv)

Part 4: Remote sensing application 2: Segmentation and classification of hyperspectral images using watershed. (pdf) (wmv)

Part 5: Practical session on mathematical morphology. (pdf)


1) Practical Sessions on Image Processing (Traitement d'Images, SICOM, 2ème année, Grenoble INP - PHELMA, France), 48h, 2008 - 2010.