CS 175: Project in Artificial Intelligence

Fall 2021

Course logistics

  • When: Tuesdays and Thursdays at 2–3:20pm
    • Lectures during the first couple of weeks will introduce reinforcement learning and suggested project platforms.
      • Some of these lectures will be in-person and some virtual; please see the schedule below for the planned (subject to change) location of each lecture.
      • These lectures are optional. This page will list online introductory resources.
    • After the introductory lectures, project teams will meet separately at times they will schedule.
    • Each team will meet with the instructor at least every other week.
      • Most virtual meetings will be during Tuesday and Thursday class hours.
      • Other time slots, including in-person ones, will also be available.
    • During week 10, the class will meet again for teams to present their projects.
    • There will be no discussion section meetings.
  • Where:
  • Announcements and forum: ed discussion
    • Important course announcements will be made on the forum.
    • Please post on the forum, publicly or privately, all course-related questions.
    • Please do not email course staff, except for personal matters unrelated to the course.
  • Assignments: gradescope
    • Assignments will be uploaded to this page.
  • Teams, reports, and grades: canvas
  • Instructor: Prof. Roy Fox
    • Office hours: calendly
    • Enrolled students are welcome to:
      • Schedule 15-minute slots (more than once if needed);
      • Give at least 4-hour notice;
      • All team members are expected to attend project meetings.
  • Teaching assistant: Kolby Nottingham

Grading policy

  • Assignments: 10% (individual)
    • Late submission: 3 grace days total, for both assignments, per person
  • Project proposal: 10% (team)
  • Progress report: 20% (team)
  • Final report: 40% (team + individual component)
    • Late submission: 4 grace days total, for all project submissions, per team
  • Project presentation: 15% (team)
  • Participation (in-class, on-forum, evaluations): 5% (individual)
  • No exams

Schedule

(Week) Dates Tuesday Thursday
(0) Sep 23 Introduction (in-person)
Prof. Singh's videos
(1) Sep 28, 30 Reinforcement Learning (in-person)
Prof. Singh's videos
Platforms (virtual)
(2) Oct 5, 7 Deep Reinforcement Learning (virtual) Project meetings
Assignment 1 due
(3) Oct 12, 14 Project meetings Project meetings
Project proposal due
(4) Oct 19, 21 Project meetings Project meetings
Assignment 2 due
(5) Oct 26, 28 Project meetings Project meetings
(6) Nov 2, 4 Project meetings Project meetings
(7) Nov 9, 11 Project meetings -- Veterans Day --
(8) Nov 16, 18 Project meetings
Progress report due
Project meetings
(9) Nov 23, 25 Project meetings -- Thanksgiving --
(10) Nov 30, Dec 2 Project presentations (hybrid) Project presentations (hybrid)
(11) Dec 9 Project report due

Note: the planned schedule is subject to change.

Resources

Platforms
RL libraries
Courses
Books

Academic honesty

Don’t cheat. Academic honesty is a requirement for passing this class. Compromising the academic integrity of this course is subject to a failing grade. The work you submit must be your own. Academic dishonesty includes, among other things, partially copying answers from other students or online resources, allowing other students to partially copy your answers, communicating information about exam answers to other students during an exam, or attempting to use disallowed notes or other aids during an exam. If you do so, you will be in violation of the UCI Policy on Academic Honesty and the ICS Policy on Academic Honesty. It is your responsibility to read and understand these policies, in light of UCI’s definitions and examples of academic misconduct. Note that any instance of academic dishonesty will be reported to the Academic Integrity Administrative Office for disciplinary action, and may fail the course.