Artificial Intelligence for Social Impact
(CPSC 532L/530L, Term 2, Session 201, 2019-20)

Overview

Grading Scheme

Schedule

 

Overview

Course Description: This course will focus on the application of cutting-edge technologies from artificial intelligence to solve practical problems faced by a nonprofit partner in our own local community. Our 2020 partner is not yet finalized. In 2019 our partner was CityStudio Vancouver, "an innovation hub at the heart of City Hall where city staff, students, faculty and community work together to design experimental projects that make Vancouver more sustainable, liveable and joyful."

 

This course takes a group of students with existing technical skill in AI and helps them to examine everything else that is required to make a real world impact. In particular, we will take an experiential learning approach to examine building a relationship with a client, problem identification, refining goals as new information becomes available, and communicating project progress. The course will also emphasize the use of modern tools and workflows for remote collaboration. Putting these tools into practice, the course will be remotely co-taught by Prof. Scott Kominers of the Harvard Business School. He will help to guide us in using the Case Method for some of our initial class discussions.

 

Prerequisites: Critical to the idea of the course is the premise that all enrollees have existing AI expertise that enables them to focus on choosing the right techniques for our partner's problem and applying these ideas in practice rather than learning the techniques themselves. Such previous experience with AI may include courses, research/internship/course projects, hobbies, work experience, etc. Additionally, an ability to speak, read and write fluently in English is essential for success in the class. All enrollees begin by signing up for the waitlist, contact the instructor personally, and are then transferred to the main course as appropriate.

 

Course number: The course is cross-listed as CPSC 532L (Topics in Artificial Intelligence, part of the department's "Computational Intelligence" stream) and CPSC 530L (Topics in Information Processing, part of the department's "Interdisciplinary Studies" stream). Students are free to enroll in whichever course better suits their needs.

 

Equity, Inclusion and Wellness: Please see the CS Department's resources on this topic.

 

Academic Honesty: Plagiarism is a serious offence (see the CS Department's statement) and will be dealt with harshly.  I consider plagiarism to be the unattributed use of an external source (e.g., another student, code or text from a web site, a book) in work for which a student takes credit, or the inappropriate use of an external source whether or not attribution is made. The seriousness of the offence depends on the extent to which the student relied upon the external source.  You must cite all external sources that you use, and write in your own words. Any text that you take verbatim from another source must be in quotation marks and followed by a citation.

Grading Scheme

We will revisit this grading scheme once the course begins; what follows is one possibility.

 

Broadly speaking, grades will be divided into three categories:

1. Participation in class
2. Contributions to the project prototype (mathematical underpinnings, code, UI, etc)
3. Authorship of joint presentations and the final report

Participation in class (1) will be 25% of the final grade, determined through a mix of peer assessment and assessment by the instructors. (Active involvement in the peer assessment process is one form of participation expected of all students.) Each of contributions to the project (2) and authorship of presentations and the final report (3) will be allocated another 25%. Each student will be free to choose any way of splitting the remaining 25% between (2) and (3).

 

Schedule

A draft schedule will be posted closer to the start of term.