List of Projects - Fall 2019

Posted on January 21, 2020

Invited jury members
Alix Lhéritier, Amadeus SAS
Marco Lorenzi, INRIA
Serena Villata, CNRS

Best project presentation award

Clustering of DNA strings
R Schiavone, A Senacheribbe

Honorable mentions

Choosing the right tool to do upper airway surgery
E Arriau, P Luscan

Broken Bones Detection
I Moschini, T Nait Saada

Deep Neural Networks with Flexible Rational Activation Functions
A Bemani

Machine learning techniques to play snake
M Bouthors, A Barral

Bone X-Ray Classification: Final Report
C Gohlke, S El Hadramy

Autonomous Driving Processing Pipeline
M Da Silva–Filarder, U Lecerf

Clustering users according to their interests
F Brunero, M Guarrera

All projects

Predicting the likelihood of drug consumption

Keyword/Concept Identification

Prototyping a recommendation system for user rating predictions of movies

Football match prediction based on FIFA statistics

Predicting Football Result via ML

Convolution Neural Network in Facial Expression Recognition

Text Classification

Collaborative and content based movie recommendations

Music Genre Classification

Flight Delay Prediction

House Prices: Advanced Regression Techniques

Beating the bookmakers

Road Signs Classification with Machine Learning

Image Captioning

Movie Recommender System

Daily Trading the S&P500 using a crossmomentum strategy

Optimize Inventory

Digit recognizer: Implementation and comparison of various ML algorithms on MNIST

Success Movie

Sentiment Analysis on Tweets

Predicting the influencer

Bitcoin stock market trend prediction

Political Social Media Posts

Intelligent Wine Analysis

Predicting Fake Content

Titanic: Machine Learning from Disaster

Prediction of music listening

Bike Share Demand

Image classification of memes - recognizing pepe the frog with a CNN and SVM combination

Netflix Movie Rating Predictor

Pattern recognition in Psychedelic music

Balance Eye

Hybrid recommender for movies rating

Pollution Level Forecasting

Determining the chances of a successful climbing depending on previous climbs and weather conditions