TRandrianarisoa

community-project

Project on Community Detection in Networks as part of the Compressed Sensing class at the ENSAE ParisTech (Third Year))

academic_webpage

BayesTrees

MCMC simulation from the a posteriori distribution of Bayesian regression Trees.

community-project

Project on Community Detection in Networks as part of the Compressed Sensing class at the ENSAE ParisTech (Third Year))

Financial-Time-Series-with-GANs

This project is part of the "Machine Learning for Finance" course conducted by Romuald Elie at ENSAE Paris. In this project we explored different Generative Adversarial Networks architectures in order to generate financial Time Series.

Kangaroos

Particle Sequential Monte Carlo

Memory_Efficient_Kernel_Approximation

During this project, we reviewed the article Memory Efficient Kernel Approximation from S. Si, C-J. Hsieh and I. Dhillon (2015). In this article, the authors dwell on the advantages and drawbacks of different methods used to approximate a Gram matrix.

NYCTaxiFare

Prediction of Taxi Fare in the city of New York

Preference_Learning

WorkplaceEng

Analysis of a dataset on employee data - Predict the attrition probability

TRandrianarisoa/BayesTrees

MCMC simulation from the a posteriori distribution of Bayesian regression Trees.

TRandrianarisoa/academic_webpage

TRandrianarisoa/community-project

Project on Community Detection in Networks as part of the Compressed Sensing class at the ENSAE ParisTech (Third Year))

TRandrianarisoa/Financial-Time-Series-with-GANs

This project is part of the "Machine Learning for Finance" course conducted by Romuald Elie at ENSAE Paris. In this project we explored different Generative Adversarial Networks architectures in order to generate financial Time Series.

TRandrianarisoa/Kangaroos

Particle Sequential Monte Carlo

TRandrianarisoa/Memory_Efficient_Kernel_Approximation

During this project, we reviewed the article Memory Efficient Kernel Approximation from S. Si, C-J. Hsieh and I. Dhillon (2015). In this article, the authors dwell on the advantages and drawbacks of different methods used to approximate a Gram matrix.

TRandrianarisoa/NYCTaxiFare

Prediction of Taxi Fare in the city of New York

TRandrianarisoa/Preference_Learning

TRandrianarisoa/WorkplaceEng

Analysis of a dataset on employee data - Predict the attrition probability