Schedule

• Time: 1:15 - 2:45 pm, Wednesday
• Dates: Oct 16, 23, 30; Nov 6, 13, 27; Dec 4, 11, 18; Jan 8, 15, 22, 29
• Room: Tachibana-Kaikan (near Koshibo)

This initiative aims to replace traditional book-based learning with hands-on, collaborative projects that allow you to explore advanced concepts in Graph Learning and Image Processing. Here are the detailed guidelines and instructions to ensure your project is well-structured and aligns with the initiative's goals:

Total Members: 7 students, divided into two specialized groups.

Group A: students focusing on Graph Learning Research.

ArchVision AI: students focusing on Image Processing Research.

The primary objective is to foster a shift from passive learning to active project-based learning. This approach enables you to:

• Deepen theoretical understanding through practical application.
• Engage in projects that align with your research focus area.

Criteria for Project Selection:

• Relevance to the group’s specific focus area (Graph Learning or Image Processing).
• Should address a significant problem or explore an innovative concept.
• Feasible to complete within a semester.

Example Project Idea: Using Neural Architecture Search to find the best architecture for image classification.

Timeline: Projects must be completed within one semester.

Collaboration Platform: Use GitHub for version control

Roles of All Members:

• Research relevant papers and source code.
• Contribute to the project’s codebase, documentation, and other materials on GitHub.
• Participate in weekly meetings to discuss progress and address challenges.

Individual Responsibilities:

Literature Review: Each member is assigned specific resources to review and present summaries.

Code Implementation: Members focus on different modules or features to ensure full project coverage.

Minimum Outcome: Successful replication of a recent research paper to code implementation.

Desired Outcomes (If possible):

Potential publication of robust and novel results in academic journals or conferences.

After completion:

• Enhance coding and project management skills.
• Develop critical thinking and problem-solving abilities through real-world applications.
• Build a portfolio on GitHub to showcase skills to potential employers.
• Demonstrate teamwork and collaborative skills.

Resilience in Outcomes:

• Even if the project is not publishable, it serves as a valuable addition to your professional portfolio.

Progress Tracking: Use GitHub Issues and Projects to manage tasks, milestones, and deadlines.

Regular Check-ins: Weekly meetings for updates, insights, and problem resolution.

Final Presentation: At the semester's end, present the project outcomes, highlighting achievements, challenges, and lessons learned.

Thank you. Enjoy Learning

Schedule

• Default: 1:15 - 2:45 pm, Monday
• Room: 121/122 実習室
• April 8, 15, 22
• May 13, 20, 27
• June 3, 10, 17, 24
• July 1, 22, 29

Book

• https://www.cs.mcgill.ca/~wlh/grl_book/ (each time about 10 pages)