David Picard

IMAGINE/LIGM, École des Ponts ParisTech
CNRS, Univ Gustave Eiffel
6-8, Av Blaise Pascal - Cité Descartes
Champs-sur-Marne
77455 Marne-la-Vallée cedex 2

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Teaching 2022

Introduction to machine learning (MALAP). 6 lectures + 4 lab sessions (with the goal of implementing actual algorithms).

  • All slides in book format: pdf
  • 1. Introduction, ERM, k-NN: html, ipynb
  • 2. Linear models: html, ipynb
  • 3. SVM and kernels: html, ipynb
  • 4. Decision trees and ensembling: html, ipynb
  • 5. Neural networks: html, ipynb
  • 6. Clustering and metric learning: html, ipynb
  • Lab 0. Numerical algebra: ipynb
  • Lab 1. Nearest neighbor classifier: ipynb
  • Lab 2. Support vector machines: ipynb
  • Lab 3. Decision trees: ipynb
  • Lab 4. Metric learning: ipynb



Design and source code from Jon Barron's website