UE Information access and retrieval

User information

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Degrees incorporating this pedagocial element :


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.


Course contents:

Part I. Foundations of Information Retrieval

Course 1: Information retrieval basics.
Course 2: Classical models for information retrieval.
Course 3: Natural language processing for information retrieval.
Course 4: Theoretical models for information retrieval.

Part II: Web and social networks

Course 5: Web information retrieval and evaluation.
Course 6: Social networks and information retrieval.
Course 7: Personalized and mobile information retrieval.
Course 8: Recommender systems.

Part III: Multimedia indexing and retrieval

Course 9: Visual content representation and retrieval.
Course 10: Classical machine Learning for multimedia indexing.
Course 11: Deep learning for information retrieval.
Course 12: Deep learning for multimedia indexing and retrieval.

Recommended prerequisite

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.