Nelle Varoquaux
f25a4e7c4c
Adding the links to the README
|
il y a 2 ans | |
|---|---|---|
| data | il y a 3 ans | |
| 01-PCA.ipynb | il y a 3 ans | |
| 02-FeatureProcessing.ipynb | il y a 3 ans | |
| 03-ProjectIntro.ipynb | il y a 2 ans | |
| 04-linear-and-logistic-regression.ipynb | il y a 2 ans | |
| 05-Regularization.ipynb | il y a 2 ans | |
| 06-TreesAndForests.ipynb | il y a 2 ans | |
| 07-NearestNeighbors.ipynb | il y a 2 ans | |
| LICENSE | il y a 3 ans | |
| README.md | il y a 2 ans | |
| environment.yml | il y a 3 ans |
README.md
hpc-ai-ml-2022
This repository holds the computer labs for the Introduction to Machine Learning course of the 2021-2022 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: Cross validation & features
- Lab 4: Linear and logistic regression (1h)
- Lab 5: Regularized models (1h)
- Lab 6: Trees and Forests
- Lab 7: k-NN
Acknowledgements
These notebooks are adapted from notebooks previously created with the help of Chloé-Agathe Azencott, Judith Abecassis, Joseph Boyd, Arthur Imber, Benoit Playe and Mihir.