Algorithmic Machine Learning

This repository contains Jupyter Notebooks for the Algorithmic Machine Learning Course at Eurecom.

These notebooks are jointly completed by Alessandro Gaballo and Daniele Reda.

Notes on the notebooks

[Lecture 3+4] Recommender algorithm - Music recommendations
[Lecture 5+6] Regression algorithm - Using Random Forests to predict Airplane Delays
[Lecture 7+8] Estimating Financial Risk through Monte Carlo Simulation
[Lecture 9+10] Anomaly Detection in Network Traffic with K-means clustering
[Lecture 11+12] Analyzing Neuroimaging Data with PySpark and Thunder

Note: [Lecture 1+2] were just an introduction to the course with no notebook to complete.

Name		Name	Last commit message	Last commit date
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html_versions		html_versions
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README.md		README.md
[Lecture 11+12] Analyzing Neuroimaging Data with PySpark and Thunder.ipynb		[Lecture 11+12] Analyzing Neuroimaging Data with PySpark and Thunder.ipynb
[Lecture 3+4] Recommender algorithm - Music recommendations.ipynb		[Lecture 3+4] Recommender algorithm - Music recommendations.ipynb
[Lecture 5+6] Regression algorithm - Using Random Forests to predict Airplane Delays.ipynb		[Lecture 5+6] Regression algorithm - Using Random Forests to predict Airplane Delays.ipynb
[Lecture 7+8] Estimating Financial Risk through Monte Carlo Simulation.ipynb		[Lecture 7+8] Estimating Financial Risk through Monte Carlo Simulation.ipynb
[Lecture 9+10] Anomaly Detection in Network Traffic with K-means clustering.ipynb		[Lecture 9+10] Anomaly Detection in Network Traffic with K-means clustering.ipynb