Teaching
- 2021-2022
- Modelling and Optimization in Machine Learning: Exercises classes and labworks (Polytech Nice MAM4/EIT Digital)
- Introduction to Artificial Intelligence: Classes and labworks (first 4 classes - EUR DS4H/SPECTRUM/LIFE) Moodle's link
- Signals and Systems: Classes, exercises classes and labworks (Polytech Nice PeiP2) Moodle's link
- Augmented Reality: Classes and exercises classes (Polytech Nice MAM4/SI4) Moodle's link
- Statistics and R: Classes and exercises classes (Polytech Nice MAM4/SI4) Moodle's link
- End of semester projects (Polytech Nice MAM4)
Proposed subjects: (Under construction)
- Final year project (Polytech Nice MAM5)
Proposed subject: Optimizing the Compression Rates in Deep Compression Methods using Bayesian Optimization
- Other courses and projects from previous years
- End of semester projects (Polytech Nice MAM4)
Proposed subjects: Decompositions of a Small Tensor
Multidimensional Data Compression using Quantization of Low-Rank Tensor Models
Data Visualization using the Distance Correlation
Development of a python module for tensor decompositions (in French)
Concepts visualization with google similarity metric (in French)
Movie recommandation with matrices and tensors (in French)
Data Visualization using the Distance Correlation
Development of a python module for tensor decompositions (in French)
Concepts visualization with google similarity metric (in French)
Movie recommandation with matrices and tensors (in French)
- Final year projects (Polytech Nice MAM5/M2 EIT DIGITAL):
Proposed subjects: Gradient algorithms for co-clustering and higher-order co-clustering
Model-Based Clustering with Symmetric Tensor Decompositions
Tensor and matrix factorizations in python: application to recommender systems
Model-Based Clustering with Symmetric Tensor Decompositions
Tensor and matrix factorizations in python: application to recommender systems
- Big Data Technologies: Labworks (M1/M2 EIT Digital)
- Large-scale Distributed Computing Systems: Labworks (M1/M2 EIT Digital)
- Tensor Decompositions and Applications (M2 Data Science UCA)
- Machine Learning for Big Data: Classes and exercises classes (Polytech Nice SI4)
- Data processing and statistics: Exercises classes (Polytech Nice SI3)
- Mathematics: Exercises classes (Grenoble INP - ENSE3 1st year)
- Digital transmission systems: Labworks (Grenoble INP - M2 CSE ENSIMAG)