Christophe Ambroise

  • Professor, Evry University, France
  • Talk: Statistical learning of stochastic latent block models for networks inference

Peter Tino

  • Pofessor, University of Birmingham, UK
  • Talk: Probabilistic Modelling in Machine Learning

Romain Hérault

  • Associate Pofessor, National Institute of Applied Sciences of Rouen, France
  • Talk: Deep Learning

Jalal Fadili

  • Professor, Ensicaen & Insitut Universitaire de France (IUF), France
  • Talk: Sparse representation of high dimensional signals and images

Hien Nguyen

  • Australian Research Council DECRA Research Fellow, La Trobe University, Australia
  • Talk: An introduction to MM algorithms for the machine learning and statistical estimation
Abstract: MM (majorization-minimization) algorithms are an increasingly popular tool for solving optimization problems in machine learning and statistical estimation. This lecture introduces the MM algorithm framework in general and via three commonly considered example applications: Gaussian mixture models, multinomial logistic regressions, and support vector machines. Specific algorithms for these three examples are derived and numerical demonstrations are presented. Theoretical and practical aspects of MM algorithm design are discussed.

Mustapha Lebbah

  • Associate Professor, Paris 13 University
  • Talk: Scalable machine learning and distributed systems

Faicel Chamroukhi

  • Professor, Caen University, France
  • Talk: Unsupervised learning of latent variable models from high-dimensional data