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Large­Scale Machine Learning & Data Mining

Sigle: MINES20, ECTS: 2

Objectifs du cours

Machine learning is a fast­growing field at the interface of mathematics, computer science and engineering, which provides computers with the ability to learn without being explicitly programmed, in order to make predictions or take rational actions. From cancer research to finance, natural language processing, marketing or self­driving cars, many fields are nowadays impacted by recent progress in machine learning algorithms that benefit from the ability to collect huge amounts of data and “learn” from them. The goal of this intensive 5­day course is to present the theoretical foundations and practical algorithms to implement and solve large­scale machine learning and data mining problems, and to expose the students to current applications and challenges of “big data” in science and industry.

Pré-requis

Students are expected to have working knowledge of basic linear algebra, probability, optimization and programming in Python. They should also already be familiar with the basics of machine learning (general principles and methodology, SVM, neural networks, binary decision trees, boosting, ...). Therefore, this course is normally intended for students in 3rd year, who have already attended 2nd year course “Apprentissage artificiel” proposed in bloc4C (~may) at MINES ParisTech. Of course, students who have attended elsewhere (e.g. in S3 abroad, or via a MOOC) a machine-learning course with a content roughly equivalent to  “Apprentissage artificiel” are also welcome to this ADVANCED course focusing on LARGE-SCALE issues in Machine-Learning.
This course is open in priority to 3rd­year students at MINES ParisTech, and to all students and researchers at PSL pending on space availability.

Programme

The week is organized around three types of activities:

  • Lectures (morning)
  • Practical sessions (afternoon)
  • Conference and round tables (evenings) 

Equipe pédagogique

Responsable(s)
Fabien MOUTARDEJean-Philippe VERT

Chargé(s) d'enseignement
Guillaume DEVINEAUXavier ROYNARDHugues THOMAS

Intervenant(s)
Chloé-Agathe AZENCOTT ; Francis BACH ; Marco CUTURI ; Akin KAZAKCI ; Laurent LAUDINET
Sigle MINES20
Année 2ème & 3ème année
Niveau Graduate 1st year, Graduate 2nd year
Crédits ECTS 2
Coefficient 2
Nb. d'heures 37
Nb. de séances 30
Type de cours Descriptifs complémentaires
Semestre 4, 6
Période Printemps
Domaines
  • Mathématiques
  • Mathématiques appliquées et calcul
  • Probabilités et statistiques
  • Informatique
Dernière mise à jour:
20 Jun 2017 11:14 par julien