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  • First Class will be on
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Course Overview

Affective computing is the study of how emotions can have a major impact within intelligent interactive computer systems. Emotions are known to be central and basic to human interaction. Affective computing researchers attempt to bring emotions into artificial systems that interact with humans. Research in this area focusses on four primary areas. First, the study of basic theories of emotion, from psychological, sociological, and neuroscientific perspectives. Second, the study of techniques to recognise emotion from psysiological signals including speech, heartrate, skin conductance, eye gaze and body posture. Third, the study of how to generate believable emotional signals in virtual agents, including embodied conversational agents, avatars, assistive agents, and chatbots. Fourth, the study of how to implement theories of emotion in particular domains, and how to integrate recognition and generation of affect to make more efficent, believable, enjoyable and useful intelligent interactive systems.
See also a video description of the class


Important Dates


The course will be a combination of lectures by the instructor, hands-on practical experience with APIs and datasets, and then student-led presentation and discussion of recent research papers. With the research papers, students will be responsible for presenting them in class and discussing them. The papers presented should have something to do with the student's course project. Projects will also be presented in class at the end of the semester.

The first 9 weeks of class will be one lecture by the instructor + discussion, and each student must submit a public (to the class) 100-word summary on the Slack channel about each lecture by the end of the week. Further, there will be a series of assignments requiring hands-on work with APIs and datasets (to be announced). These will be completed roughly bi-weekly for the first 9 weeks. Following this, students will present their projects or a related paper. Each student will have 10 minutes to present, and each student must also write a 100-word summary of at least two other student presentations.


The grading breakdown is subject to change. The last 10% will be allocated for overall course participation. This will be judged by the instructor based on number of non-required (as above) contributions on Slack or in class (either is good). Group projects may be OK by permission from instructor.


The course will be relevant to researchers in human-computer interaction and in artificial intelligence. There are no formal pre-requisites, as the field is inherently multi-disciplinary and requires breadth across disciplines including AI, sociology and psychology. Some of the topics involve some mathematics, but the students will be given the necessary background during the course. The ideal project is one that will show how affective reasoning can be used in the student's research, regardless of the major area.

Academic Integrity

Academic Integrity: In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. All members of the UW community are expected to hold to the highest standard of academic integrity in their studies, teaching, and research. The Office of Academic Integrity's website ( contains detailed information on UW policy for students and faculty. This site explains why academic integrity is important and how students can avoid academic misconduct. It also identifies resources available on campus for students and faculty to help achieve academic integrity in and out of the classroom.

Grievance: A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70 - Student Petitions and Grievances, Section 4,

Discipline: A student is expected to know what constitutes academic integrity, to avoid committing academic offenses, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offense, or who needs help in learning how to avoid offenses (e.g., plagiarism, cheating) or about rules for group work/collaboration should seek guidance from the course professor, academic advisor, or the Undergraduate Associate Dean. When misconduct has been found to have occurred, disciplinary penalties will be imposed under Policy 71 Student Discipline. For information on categories of offenses and types of penalties, students should refer to Policy 71 - Student Discipline,

Avoiding Academic Offenses: Most students are unaware of the line between acceptable and unacceptable academic behaviour, especially when discussing assignments with classmates and using the work of other students. For information on commonly misunderstood academic offenses and how to avoid them, students should refer to the Faculty of Mathematics Cheating and Student Academic Discipline Policy,

Appeals: A student may appeal the finding and/or penalty in a decision made under Policy 70 - Student Petitions and Grievances (other than regarding a petition) or Policy 71 - Student Discipline if a ground for an appeal can be established. Read Policy 72 - Student Appeals,