ML Group Mines ParisTech 2016-17 Fontainebleau, France

Week 1 - Introduction - R. Alais

Slides: "Introduction au Machine Learning"

Additional content:

  • Chapter 1 of Murphy's book.

  • A Few Useful Things to Know about Machine Learning., P. Domingos, 2012

  • Practical advice for applying Machine Learning (adapted from Andrew Ng lectures)

  • Week 2 - Decision Trees and Random Forests - S. Drouyer

    Coming soon.

    Week 4 - SVM and Kernel Methods - M. Demangeot

    Slides : "Support Vector Machines" - Related code here

    Additional content:

  • Lecture notes on SVM , Andrew Ng

  • Tutorials on SVMs and Kernels

  • Week 5 - Jobs in ML

    Slides :

  • Mind map on jobs in ML (note: install mindmapmaker on drive for it to work), S. Drouyer
  • "Data scientist and Machine Learning engineers : who’s who?", M. Pereira
  • "ML and computer science", P. Guillou
  • Additional content:

  • Where machines could replace humans — and where they can’t (yet), Michael Chui, James Manyika, and Mehdi Miremadi

  • Week 8 - Convolutional Neural Networks - R. Alais

    Slides : "Convolutional Neural Networks"

    Additional content:

  • Hands-on TensorBoard (TensorFlow Dev Summit 2017)

  • CS231n: Convolutional Neural Networks for Visual Recognition, Stanford CS class notes

  • Intro to Deep Learning (Udacity Nanodegree), Siraj Raval

  • Week 9 - Some Uses of Graphs in Machine Learning - M. Pereira

    Slides : "Some Uses of Graphs in Machine Learning" - Related code here (in R)

    Additional content:

  • Graphs in Machine Learning, Lectures 0 - 4, Michal Valko

  • Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions, Zhu et al.

  • On Spectral Clustering: Analysis and algorithm, Ng et al.

  • Week 10 - Fantastic Architectures and How to Use Them - P. Guillou

    Slides : "Fantastic Architectures and How to Use Them" - Related code here

    Additional content:

    Coming soon.