LE/EECS 4401 3.0 Artificial Intelligence
GS/EECS 5326 3.0 Topics in Artificial Intelligence
Winter 2020

Department of Electrical Engineering & Computer Science,
York University

Course Project

Proposal (worth 5% of course grade) due: March 17 in class
Presentation (worth 10% of course grade) due: March 31/April 2
Final report (worth 20% of course grade) due: April 20

This course requires doing a project in the area of artificial intelligence, on a topic related to the material covered in the course. The project may involve doing a literature survey, an evaluation of a an AI solver or tool, an implementation of an AI method, or even a research paper on an AI problem and a proposed solution. Some topics suggestions appear below, classified according to the type of project. Projects in EECS 4401 may be done individually or in teams of two students; in EECS 5326, the project must be individual. For team projects, each student must be responsible for a distinct part of the project presentation and a distinct part of the final report, which will be graded separately.

Project Deliverables

By November 17, you should select a topic and submit a short (one or two pages) project proposal. The proposal should outline your topic, state the project's objectives, give a tentative timetable for the tasks to be accomplished, and include a list of references (especially important for literature surveys). For implementation projects, it is recommended that a preliminary design for the system be included. You are encouraged to discuss your project with the instructor in advance. You will also get feedback on your proposal.

On March 31/April 2 students will give a short (15-20 min.) class presentation on their project and answer questions (you will be given a time slot in advance).

Your final report on the project is due by December 20. For literature survey projects, include an overview of the topic area, a discussion of the papers read, and a complete list of references; critical analysis is expected. For system evaluation or implementation projects, state the project's motivation and objectives, provide a readable description the system that was implemented or evaluated, explain how the system is used, describe the testing that was performed, and discuss how usable the system is; include the documented code and/or sample tests in appendix as appropriate. Note that it is not sufficient to simply download a solver and run some of the examples included with it; you must either develop some new examples or evaluate the solver on a wide range of benchmarks.

Suggested Project Topics