Introduction to Machine Learning at CentraleSupelec (Fall 2017)
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Lab 1 2017-10-02 Principal Components Analysis.ipynb
Lab 2 2017-10-06 Convex optimization.ipynb
Lab 3 2017-10-13 Getting started on the KaggleInClass Challenge.ipynb
Lab 4 2017-10-20 Linear and logistic regression.ipynb
Lab 5 2017-11-10 Regularized linear and logistic regressions.ipynb
Lab 6 2017-11-17 Nearest neighbors.ipynb
Lab 7 2017-11-24 Decision trees and random forests.ipynb
Lab 8 2017-12-01 Support vector machines.ipynb


This repository holds the computer labs for the MA2823 course (Introduction to Machine Learning) at CentraleSupelec in Fall 2017. The course website is at

Getting the labs

Each lab is set up as a Jupyter notebook (.ipynb file). Although these will display in read mode when you click on them, you should download them locally to your computer in order to run and modify them. You can do this directly from the webpage, but we recommend forking then cloning the repository. If you are new to git and GitHub, you will find detailed instructions in the syllabus.

Setting up your computer

The labs require Python2.7 (Python 3.3 is fine as well, but you might encounter some minor issues with e.g. print statements). All required packages are part of the Anaconda platform so you can simply install Anaconda on your machine.

If you'd rather install just the required packages with pip, that is also possible. You will need:

To check your installation, try launching Jupyter (e.g. by typing jupyter noteboook in a shell), navigating to where you have downloaded/pulled the first lab (.ipynb file) and opening it. In that notebook (or in a python terminal), you should be able to run

import sklearn
import pandas
import seaborn
import matplotlib