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. Each part is illustrated on examples associated with different applications.
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.
This course requires basic knowledge of probability and integration theory, of linear algebra and of differential calculus.
EXAM1: Written examination
EXAM2: Oral or written examination
N1 = EXAM1
N2 = EXAM2
The exam is given in english only
The course exists in the following branches:
Course ID : WMM9MO23
You can find this course among all other courses.
Date of update January 15, 2017