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
Communication
- All lectures are in person.
- All communication should take place using the
Slack group. You will receive an invitation to sign up in the first week of class.
- Public Slack posts are the preferred method for questions about course material, etc. Students can then help each other and instructors can read/reply.
- All project proposals and reports will be handed in on LEARN
- You can also send me email with personal questions
- I will be available for discussions (office hours) on Tuesdays at 2pm in DC3613
Important Dates
- See Full Schedule for a detailed list of readings for each lecture
- October 12th Project proposals due.
- December 3rd Student presentation summaries due
- December 3rd Project reports due
Organization
The course will be a combination of lectures by the instructor,
invited lectures,and student project presentations.
Projects will also be presented in class at the end of the semester.
Each week of class will consist of two lectures by the instructor or invited speaker + discussion, and each student must submit a public (to the class) 100-200 word summary on the Slack channel per week about the lectures of that week. Summaries must be submitted by the start of the following week. Each student must submit a total of 6 summaries, plus 2 summaries of other student presentations at the end of class.
Following this, students will present their projects. Each student will have 10-15 minutes (TBD) to present, and each student must also write a 100-200 word summary of at least two other student presentations.
Grading
- Student presentations and two summaries of other student presentations (25% total: 15% presentation, 5% for each summary)
- Individual project (35% -- 5% proposal, 30% report) see here for details
- Lecture summaries (6x5%=30%)
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.
Prerequisites
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 (
www.uwaterloo.ca/academicintegrity)
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,
http://www.adm.uwaterloo.ca/infosec/Policies/policy70.htm
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,
http://www.adm.uwaterloo.ca/infosec/Policies/policy71.htm
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,
http://www.math.uwaterloo.ca/navigation/Current/cheating_policy.shtml
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,
http://www.adm.uwaterloo.ca/infosec/Policies/policy72