After the completion of MachineLearnAthon, the following resources and features will be made available:
- Didactic concept and MachineLearnAthon evaluation: A comprehensive framework and assessment methodology will be provided to support the educational aspects of MachineLearnAthon.
- Pre-challenge micro-lectures and ML tool introduction units: Educational materials will be created and shared, offering instructional content and introducing participants to various ML tools.
- ML use-cases and real-world data: Engaging use cases will be developed, and real-world datasets will be prepared to provide practical and relevant learning experiences.
- Different difficulty level challenges: Tasks within the use cases will be designed with varying levels of difficulty, ensuring that students with limited or no prior ML knowledge can participate and contribute their domain expertise.
- Course internal leaderboards: A system will be implemented to establish course internal leaderboards, enabling the evaluation and tracking of the current results and progress of all participating teams throughout the duration of the course.