This class was intially designed for the preparatory week of PSL. In this repo, some adjustment are made to fit it to an audience of Criteo bootcampers.
The dedicated slack channel is #2025-bootcamp. Please use the channel to ask questions or if you need help to solve the exercices.
Day 1
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9:00--10:20: (course) Machine learning: recent successes. Machine learning: history, application, successes
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10:40-12:00: (course) Introduction to machine learning. Introduction to machine learning
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14:00--15:30: (course) Machine learning models (linear, trees, neural networks). Supervised machine learning models
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16:00-17:30: (course) Scikit-learn: estimation/prediction/transformation. Scikit-learn: estimation and pipelines
These practical sessions will necessitate the use of Python 3 with the standard Scipy ecosystem, Scikit-learn and Pytorch. They will make use of Jupyter notebooks. The easiest way to proceed is to have a gmail account and make use of a remote Google Colab to run the notebooks.
Day 1 (at home)
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(lab session) Introduction to Python and Numpy for data sciences.
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(lab session) Practice of Scikit-learn.
Day 2 (at home)
- (lab session) Logistic regression with gradient descent.
- Optimization and the Corrected notebook
Day 3 (at home)
- (lab session) Classification with Pytorch and GPUs
Optimization for linear models
Optimization for machine learning
Deep learning: convolutional neural networks
The slides and notebooks were originally written by Pierre Ablin, Mathieu Blondel and Arthur Mensch.
Some material of this course was borrowed and adapted:
- The slides from "Deep learning: convolutional neural networks" are adapted from Charles Ollion and Olivier Grisel's advanced course on deep learning (released under the CC-By 4.0 license).
- The first notebooks of the scikit-learn tutorial are taken from Jake Van der Plas tutorial.
All the code in this repository is made available under the MIT license unless otherwise noted.
The slides are published under the terms of the CC-By 4.0 license.