Course Overview

This course addresses advanced aspects of information access and retrieval, focusing on several points: models (probabilistic, vector-space and logical), multimedia indexing, web information retrieval, and their links with machine learning. These last parts provide opportunities to present the processing of large amount of partially structured data. Each part is illustrated on examples associated with different applications.

http://ensimag.grenoble-inp.fr/fr/formation/information-access-and-retrieval-wmm533u

Learning Achievement

Competence

The domain of information access encompasses several applications pertaining to categorization, clustering or information retrieval. The goal of this module is to present models and algorithms used in these frameworks, and is intended to students willing to use or develop tools for data mining, machine learning and information retrieval.

Course prerequisites

This course requires knowledge of probability and integration theory. Some previous knowledge of Stochastic processes is welcomed. No previous knowledge of Brownian motion or Stochastic Calculus is required.

Grading Philosophy

Final exam

Course schedule

Course type

Lecture

Online Course Requirement

Instructor

Georges Quenot

Other information

Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
- April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
- September 1st, 2020 for Spring Semester intake (February – June).

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