Affective Computing

Course Offerings

  1. Winter 2021: CSE661/DES507
  2. Winter 2020: CSE661

Course Overview

Affective Computing focuses on enabling the machines with emotion recognition and adaptive interaction. It lies in the intersection of Computer Science, design and human psychology. This course will overview the emotion theory, computational modelling of emotions, analysis of emotions using different modalities (such as voice, facial expressions, physiological signals etc) and related machine learning and/or signal processing techniques. We will also discuss ethical, legal and social implications of affective computing particularly in relation to Human-Machine Interaction.

Course Objectives

  • Students will be able to demonstrate the basic understandings of the state of the art concepts from computer science and psychology in relation to the Affective Computing.
  • Students (individually and in groups) will be able to implement, compare and evaluate the effectiveness of different state of the art affect analysis methods.
  • Students (individually and in groups) will be able to design their own method (data collection, design, development and evaluation) for affect analysis in at-least one of the fields of Affective Computing.
  • Students will be able to predict the possible consequences of affective computing. Students will also be able to offer their own set of Ethical Legal and Social rules for the development of the affect recognition.

Course Syllabus

  1. Introduction: Fundamentals of Affective Computing; Emotion Theory, Emotional Design
  2. Experimental Design: Affect Elicitation; Research and Development Tools in Affective Computing
  3. Affect Design and Detection: Emotions in Voice
  4. Affect Design and Detection: Emotions in Facial Expressions
  5. Affect Design and Detection: Emotions in Text
  6. Affect Design and Detection: Emotions in Physiological Signals
  7. Affect Design and Detection: Emotions in Multi-modal Signals
  8. Emotional Empathy: Emotional Empathy in Agents/Machines/Robots
  9. Adaptive Emotion Recognition: Challenges and Opportunities
  10. Case Study: Updated from time to time.
  11. Ethical Issues: Ethical, legal and Social Implications of Affective Computing


Related Journals and Conferences [!Exhaustive]

Course Administration

  • Google Classroom will be used as primary mode of course administration, including general announcements, announcements/submission of assignments, doubt resolution.

    Colab will be used for Coding Assignments.
  • Course will be supported with teaching assistants (TAs). TAs will communicate their office hours individually.
  • TAs will support the course through doubt resolution, responsible for announcement and/submission of quizzes/assignments, grading with faculties supervision etc.

Plagiarism Policy

  • The course follows a zero tolerance on academic dishonesty. The course follows the IIIT-Delhi policy on Academic Dishonesty [Updated Institute policy will apply]
  • All plagiarism cases will be on record for your tenure at IIIT-Delhi
  • All code and reports will be checked for plagiarism
  • Home work theory questions may be asked in exam or quiz as it is
  • If correct in Home work and incorrect in exam, home work question will be marked zero. Make sure you know all your home work theory questions.

Auditing Requirements

  • Students need to submit all assignments and projects on time.
  • Students should achieve a grade of at least the class average.
  • Audit students are not permitted to do projects with registered students.
  • Quizzes and Exams are optional.