Algorithmic Machine Learning
Introduction
This repository contains all the projects I did with jbdrvl for the Algorithmic Machine Learning Course (see course repository) at Eurecom. The guiding thread of those projects are as follows:
- Data cleaning
- Dala analysis
- Algorithm implementation
- Algorithm applied to dataset
- Optimization and further research
Summary
- Building a music recommender system - Matrix factorisation - ALS - Implicit
- Estimating Financial Risk through Monte Carlo Simulation - MonteCarlo simulation - Time series
- Predicting House Prices - Prediction models - Cross-validation
- Study of real aeronautical data from SAFRAN - Visualization - Prediction models
Technical Aspect:
We used the EURECOM cloud computing platform to work on our Notebooks. Our cluster is managed by Zoe, which is a container-based analytics-as-a-service system that Eurecom has built. Notebooks front-end run in a user-facing container, whereas Notebooks kernel run in clusters of containers. For this course, we focus on Apache Spark kernel. All of our Notebooks are configured to use Spark Python API.
License
MIT Free software