Affective Computing, or Emotion AI, is the study and development of systems and devices that can recognize, interpret, process, and simulate human affect/emotion. Emotion is fundamental to human experience, influencing cognition, perception, and everyday tasks such as learning, communication, and even rational decision-making. However, while computers cannot detect, respond to, or simulate affect, they remain crippled in the ways that they can respond intelligently and efficiently to humans.

This course will cover methods of detecting emotion such as facial expression recognition and physiological sensing. You will develop interfaces that respond to users' emotion in intelligent ways and/or simulate human emotion. We will also read and discuss research papers pertaining to the field of Affective Computing. This couse will equip you with tools as a research scientist that are applicable to general scientific domains.

In the first half of the semester, you will work with various technologies related to Affective Computing. In the second half of the semester you will focus on a Final Project where you will develop a system that detects and responds to and/or simulates emotion in an area of your interest. You will learn how to evaluate your system scientifically. Innovative and original work can be submitted to student research competitions or for publication. There will be no exams, but there will be quizzes on the topics covered.

Please check the calendar page regularly for updates and for class lecture notes, assignments, etc.

Professor: Beste Filiz Yuksel

Email: byuksel@usfca.edu

Office: HR 412B

Office Hours:
Tuesdays 11:10-11:45am
Fridays 11-11:45am
Fridays 2:05-3pm or by appointment

Teaching Assistants:

Name: Shengcai Cheng

Email: scheng23@dons.usfca.edu

Office Hours:
Tuesdays 11:45am-12:45pm HR 413
Thursdays 11:45am-12:45pm HR 413

TA and Grader.

Name: Thomas Oropeza

Email: thomasoropeza@gmail.com

Technical support for Affectiva, Android Studio, and Unity. Will hold office hours at the start of the semester on the following days/times in LS 307 and can answer questions by email:
Tuesday Jan 30 6:30-7:30pm
Thursday Feb 1 6:30-7:30pm
Tuesday Feb 6 6:30-7:30pm
Thursday Feb 8 6:30-7:30pm

Learning Outcomes:

  • Understand and build systems in the field of affective computing as well as the theory and ethics of the field.
  • Apply technologies such as facial expression recognition and physiological sensors as part of affective systems.
  • Apply such technologies to different application domains such as mobile computing, virtual reality, and game development.
  • Build affective systems that detect, respond to, and/or simulate emotion.
  • Develop a long-term project to build an independent, affective interface which will be evaluated through scientific testing and analysis.
  • Learn how to critically read, evaluate, discuss, and write about scientific papers in this field.


In addition to the handouts and relevant readings assigned for each week, we will refer to the following texts:


This is an advanced class that involves a lot of independent work. Therefore it requires a prerequisite of CS 212 or equivalent.


The course will be graded on a A-F basis. The grade distribution will be as follows:

  • Lab Assignments: 30%
  • Final Project: 40%
  • Quizzes: 10%
  • Scientific Papers: 10%
    • Reports - 5%
    • Presentation - 5%
  • Class Participation - 9%
    • Scientific Paper Class Discussions - 5%
    • Ethics Class Debate - 3%
    • Class Participation General - 1%
  • Attendance of CS department events: 1%
The evaluation will be based on successfully finishing every assignment and report. There will be no mid-terms or finals. Grades will be assigned as follows.



Labs and projects are due at 11:59pm on the due date. For any late submissions for programming assignments, 20% of your received points will be deducted per day for two days, after which there will be no credit.

Lab Assignments
Lab assignments will introduce you to technologies and application domains in affective affective and will be completed in the first half of the semester.

Final Project
Students will work on a Final Project for the majority of the semester. Students will submit a Project Proposal in the first half of the semester as well meet with the Professor to discuss their ideas and progress. Students will present a progress presentation and final presentation as well as have code reviews. Students will evaluate their final project and write up a report on their findings.

Quizzes will be held on topics discussed in lectures and during paper presentations.

Scientific Papers
Each student will present one paper to the whole class. Each student will also submit reports on each paper presented and be expected to be part of the discussion after each paper presentation.

Class Participation
Each student will be graded on their contribution and discussion on scientific papers, to the class ethics debate, questions for guest speakers, and general class participation.

Attendance of CS Departmental Events
Each student will be expected to attend three CS department events during the Spring 2018 semester. These can include: Women in Tech, ACM Student Chapter, Colloquium Talks.

Attendance Policy

Attendance is mandatory. Absences are only excused in cases of verified family or medical emergency.

Academic Dishonesty

Students are required to follow the University's Honor Code: "As a Jesuit institution committed to cura personalis- the care and education of the whole person- USF has an obligation to embody and foster the values of honesty and integrity. USF upholds the standards of honesty and integrity from all members of the academic community. All students are expected to know and adhere to the University's Honor Code. " You can find the full text of the code online at www.usfca.edu/fogcutter.

This includes but is not limited to the following:

  • ALL assignments are to be completed individually unless specified, in writing, on the assignment. Academic dishonesty will NOT be tolerated. This is your warning! Students are encouraged to meet with me if they have questions regarding assignments or this policy. Students caught cheating will face severe penalty.
Students may:

  • receive help from the professor and the TA.
  • discuss the requirements of the assignments, the meaning of programs, or high-level algorithms with other students or outside sources. If you have any doubt with respect to what is acceptable to discuss, speak with the professor first.
Students may NOT:

  • look at another student's code.
  • look at another student's solutions to homework problems.
  • receive unapproved help from an outside source including a tutor or a family member.
  • submit code which has, in whole or in part, been copied from any other source (including another student, a web page, or another text).
  • submit solutions to problems which have, in whole or in part, been copied from any other source (including another student, a web page, or another text).

  • Any help from a source other than the professor, the lab assistant, or a TA must be acknowledged. Example sources that must be cited are a parent, a family friend, and an outside tutor.
  • If you wish to get a tutor in the course, speak with the professor.
  • Any code submitted by a student must be completely original. No portion of a student's code may be copied from any other source (including, but not limited to, another student, a web page, or another text).

  • Students caught violating the academic honesty policy will face severe penalty. A first offense will result in a zero on the assignment and a report to the Dean's office. A second offense will result in the student failing the course.

Student Support

Student Disability Services

If you are a student with a disability or disabling condition, or if you think you may have a disability, please contact USF Student Disability Services (SDS) at (415) 422-2613 within the first week of class, or immediately upon onset of disability, to speak with a disability specialist. If you are determined eligible for reasonable accommodations, please provide me with your SDS Verified Individualized Services and Accommodations (VISA) form, and we will discus your needs for this course. For more information, please visit: http://www.usfca.edu/sds or call (415) 422-2613.

Learning and Writing Center

The Learning and Writing Center (LWC) provides writing assistance to students in their academic pursuits. Services are free to students and include individual and group tutoring appointments and consultations to develop specific study strategies and approaches. Please visit http://www.usfca.edu/lwc for more information.

Center for Academic and Student Achievement (CASA)

The Center for Academic and Student Achievement (CASA) provides students with academic and personal support to promote holistic student development. Please visit http://usfca.edu/casa/ for more information.