Posts by Collection

Brainiak

Short description of portfolio item number 1

Portfolio item number 2

Short description of portfolio item number 2

Optimizing deep video representation to match brain activity

Fast shared response model for fMRI data

Local optimal transport for functional brain template estimation

Modeling Shared Responses in Neuroimaging Studies through MultiView ICA

BrainIAK: The brain imaging analysis kit

Brain topography beyond parcellations: Local gradients of functional maps

mvlearn: Multiview Machine Learning in Python

An empirical evaluation of functional alignment using inter-subject decoding

Shared Independent Component Analysis for Multi-Subject Neuroimaging

On Preemption and Learning in Stochastic Scheduling

Brainiak

Mvlearn

Scikit-Learn

FastSRM

MultiView ICA

Modeling Shared Responses in Neuroimaging Studies through MultiView ICA

Published: June 22, 2020

Shared Independent Component Analysis for Multi-Subject Neuroimaging

Published: December 05, 2021

Introduction to Linear Algebra

Undergraduate course, IUT Orsay (2018), 1900

An introduction to linear algebra concluding with an introduction to abstract vector spaces.

An introduction to graphs, formal languages and automata

Undergraduate course, IUT Orsay (2019), 1900

An introduction to graph theory concluding with an introduction to automata and formal languages.

Introduction to probability theory

Undergraduate course, IUT Orsay (2020), 1900

An introduction to probability theory concluding with an introduction to statistical tests.

Modelisation

Undergraduate course, IUT Orsay (2021), 1900

Linear algebra and coupled first order differential equations

Statistics in high dimensions

Graduate course, ENPC (2022), 1900

• Shrinkage and Stein’s phenomenon (slides)
• Shrinkage and Stein’s phenomenon (slides)
• Underdetermined systems and low rank matrices

Introduction to machine learning

Graduate course, PSL (2022), 1900

• Machine learning: recent successes
• Introduction to machine learning
• Introduction to Python and Numpy for data sciences.
• Machine learning models (linear, trees, neural networks).
• Scikit-learn: estimation/prediction/transformation
• Practice of Scikit-learn
• The linear model, optimization
• Logistic regression with gradient descent
• Introduction to Deep-Learning
• Deep learning in practice (Pytorch)

Introduction to machine learning

Graduate course, PSL (2022), 1900

• Machine learning: recent successes
• Introduction to machine learning
• Introduction to Python and Numpy for data sciences.
• Machine learning models (linear, trees, neural networks).
• Scikit-learn: estimation/prediction/transformation
• Practice of Scikit-learn
• The linear model, optimization
• Logistic regression with gradient descent
• Introduction to Deep-Learning
• Deep learning in practice (Pytorch)