Schedule
This initiative aims to learn with hands-on, collaborative projects that allow one to explore advanced concepts in Graph Learning, Image Processing, and related. Here are the detailed guidelines and instructions to ensure a project is well-structured and aligns with the initiative's goals.
Total Members: ? students, divided into ?o specialized groups.
Group A: students focusing on Graph Learning
Group B: students focusing on Image Processing.
The primary objective is to foster a shift from passive learning to active project-based learning. This approach enables you to:
Criteria for Project Selection:
Example Project Idea: ???.
Timeline: Projects must be completed within one semester.
Collaboration Platform: Use GitHub for version control
Roles of All Members:
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:
Resilience in Outcomes:
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