AI Capstone Project_Đồ án tốt nghiệp TTNT
- Tín chỉ
- 10
- Bậc
- Bachelor
- Thang điểm
- 10
- Điểm qua
- 5
Mô tả
The AI Capstone Project course is an integrative experience that consolidates the knowledge and skills acquired from the undergraduate AI curriculum into a practical, real-world application. The design of this course challenges students to apply their analytical, problem-solving, and technical skills to solve complex problems using AI technologies. Throughout this course, students will engage with all facets of the AI project development lifecycle, including problem identification, data collection and preprocessing, model design and implementation, evaluation, and deployment. Students will gain a comprehensive understanding of the professional standards and expectations required to successfully execute a project of this nature. This course is structured to be completed by small groups of 3-4 students, fostering collaboration and teamwork. Each group will be assigned a supervisor, who will offer critical support and guidance throughout the research and development process. The supervisor will assist students in refining their project ideas, conducting thorough research, developing and implementing AI solutions, composing a report on their capstone project, and preparing for the final presentation in front of the examination board. By the end of the course, students will have not only deepened their understanding of AI principles and techniques but also demonstrated their ability to apply these concepts effectively in a team-based project. This capstone project not only serves as a significant portfolio piece but also prepares students for professional careers in the field of artificial intelligence.
Phân bổ thời gian
Study hour (500h) = 33.75h contact hours (1newslot/week*15week) + 1.5h capstone project defense + 464.75h self-study
Nhiệm vụ sinh viên
(1) Select a project within the chosen AI concentration: Teams should choose a project that aligns with their interests and the scope of the AI concentration. This may involve identifying a real-world problem that can be addressed with AI solutions. (2) Divide and schedule work: Once the project is chosen, teams should allocate tasks among members based on each individual's strengths and skills. A detailed schedule should be created, outlining milestones and deadlines to ensure timely progress. (3) Attend all scheduled meetings with supervisor: Teams must commit to regular meetings with their assigned supervisor. These sessions are crucial for receiving guidance, discussing progress, resolving any issues, and making necessary adjustments to the project plan. (4) Produce document and product according to project requirements: Teams are expected to produce comprehensive documentation and develop the AI product according to the specified requirements. This includes but is not limited to, designing algorithms, coding, testing, and validating the AI model(s). Documentation should detail the problem, methodology, implementation, results, and conclusions. (5) Present and defend individual contributions in front of the examination board: Each team member must be prepared to present and individually defend their specific contributions to the project in front of the examination board. This includes explaining the rationale behind their approach, the challenges faced, and the solutions implemented. (6) Students are required to attend at least 80% of scheduled meetings with their supervisor(s) to be eligible for the thesis or capstone defense.
Công cụ
Appropriate tools for design and implementation