Opintojakson tiedot

5 ECTS credits

In English

Fall, periods 1

After completing the course, student

1. is able to explain the emotion theory and modeling

2. is able to implement algorithms for emotion recognition from visual and audio signals, and the fusion of multi-modalities

3. has the ideas of wide applications of affective computing

The history and evolution of affective computing; psychological study about emotion theory and modeling; emotion recognition from different modalities: facial expression, speech, fusion of multi-modalities; crowdsourcing study; synthesis of emotional behaviors; emotion applications.

Online teaching in Moodle/Zoom. 
Moodle: target=_blank>https://moodle.oulu.fi/course/view.php?id=325&section=0

The course consists of lectures and exercises. The final grade is based on the points from exam while there are several mandatory exercises.

Computer Science and Engineering students and other Students of the University of Oulu.

A prior programming knowledge with Python, possibly the bachelor level mathematical studies and/or some lower level intermediate studies (e.g. computer engineering or artificial intelligence courses). The recommended optional studies include the advanced level studies e.g. the pattern recognition and neural networks and/or computer vision courses.

-

All necessary material will be provided by the instructor.

The assessment of the course is based on the exam (100%) with mandatory exercises.

Read more about  assessment criteria at the University of Oulu webpage.

The course unit utilizes a numerical grading scale 1-5, zero stands for fail.

Guoying Zhao, Henglin Shi, Yante Li

No

Course work space can be found from University of Oulu Moodle platform moodle.oulu.fi.