EE-623 / 4 credits

Teacher: Odobez Jean-Marc

Language: English

Remark: Next time: Spring 2022


Frequency

Every 2 years

Summary

The course will cover different aspects of multimodal processing (complementarity vs redundancy; alignment and synchrony; fusion), with an emphasis on the analysis of people, behaviors and interactions from multimodal sensor, using statistical models and deep learning as main modeling tools.

Content

Keywords

Multimodality, human activity analysis, interactions, machine learning, computer vision, audio processing.

Learning Prerequisites

Required courses

Undergraduate-level knowledge of linear algebra, statistics, image and signal processing.

Recommended courses

Introduction to machine learning course.

Assessment methods

Written and oral.

Resources

Bibliography

Pattern Recognition and Machine Learning, C·. Bishop, Springer, 2008.

 

Ressources en bibliothèque

In the programs

  • Number of places: 20
  • Exam form: Written & Oral (session free)
  • Subject examined: Perception and learning from multimodal sensors
  • Lecture: 28 Hour(s)
  • Exercises: 10 Hour(s)
  • Practical work: 18 Hour(s)

Reference week