Michal Valko

CHARGE DE RECHERCHE EPST - machine learning
    office phone: +33 3 59 57 7801
Inria Lille - Nord Europe, equipe SequeL (bureau: A05)
michal_valko.jpg

Michal Valko

CHARGE DE RECHERCHE EPST - machine learning

Enseignements

Graphs in Machine Learning - Fall - MVA - ENS Paris-Saclay

Administrivia

  • Time: Mondays 10h30-12h30
  • Place: ENS Paris-Saclay (different lecture halls)
  • 7 or 8 lectures and 3 recitations (TD)
  • Validation: grades from TD (40%) + class project (60%)
  • Research: projects, internships (stages) and PhD. thesis at SequeL and elsewhere possible
  • Piazza: Registration (with your school email) and online class discussion on piazza.
  • TA: Pierre.Perrault#outlook.com
  • course description at MVA at ENS Paris-Saclay
  • MVA tags: content: #apprentissage, type: #méthodologique #théorique, validation: #projet #td

Main topics

  • spectral graph theory, graph Laplacians
  • semi-supervised graph-based learning
  • manifold learning
  • graphs from flat data - graph as a non-parametric basis
  • online learning with graphs
  • real world graphs scalability and approximations
  • random graphs models
  • social networks and recommender systems applications
  • large graph analysis, learning, and mining
  • vision applications (e.g., face recognition)

Intro

The graphs come handy whenever we deal with relations between the objects. This course, focused on learning, will present methods involving two main sources of graphs in ML: 1) graphs coming from networks, e.g., social, biological, technology, etc. and 2) graphs coming from flat (often vision) data, where a graph serves as a useful nonparametric basis and is an effective data representation for such tasks as: spectral clustering, manifold or semi-supervised learning. We will also discuss online decision-making on graphs, suitable for recommender systems or online advertising. Finally, we will always discuss the scalability of all approaches and learn how to address huge graphs in practice. The lectures will show not only how but mostly why things work. The students will learn relevant topics from spectral graph theory, learning theory, bandit theory, necessary mathematical concepts and the concrete graph-based approaches for typical machine learning problems. The practical sessions will provide hands-on experience on interesting applications (e.g., online face recognizer) and state-of-the-art graphs processing tools (e.g., GraphLab).

Organization

The course will feature 11 sessions, 8 lectures and 3 recitations (TD), each of them 2 hours long. There may be a special session with guest lectures and internship proposals. There may be also an extra homework with extra credit. The evaluation will be based on reports from TD and from the projects. Several project topics will be proposed but the students will be able to come with their own and they will be able to work in groups of 2-3 people. The best reference for this course are the slides from the lecture which are made to be comprehensive and there is no recommended textbook. The material we cover is mostly based on research papers, some of which very recent.