Machine-Learning-Training

hpc-ai-ml-2019

This repository holds the computer labs for the Introduction to Machine Learning course of the 2019-2020 HPC-AI MSc https://www.hpc-ai.mines-paristech.fr/

Requirements

The labs were developed for Python3. All required packages are part of the Anaconda platform so you can simply install Anaconda3 on your machine. If you'd rather install just the required packages with pip, that is also possible. The labs were developed for Python 3.4.3, with the following libraries:

• numpy 1.16.5
• scipy 1.2.2
• matplotlib 2.2.4
• pandas 0.22.0
• seaborn 0.9.0
• sklearn 0.19.2

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

Program

• Lab 1: Principal Component Analysis (1h)
• Lab 2: Data normalization (1h)
• Lab 3: Introduction to the KaggleInClass project (2h)
• Lab 4: Linear and logistic regression (1h)
• Lab 5: Regularized linear regression (1h)
• Lab 6: Nearest neighbors (1h)
• Lab 7: Trees and Forests (1h)
• Lab 8: Support Vector Machines (1h)
• Lab 9: Work on the project (3h)

Acknowledgements

These notebooks are adapted from notebooks previously created with the help of Judith Abecassis, Joseph Boyd, Arthur Imber, Benoit Playe and Mihir