After the completion of MachineLearnAthon, the following resources and features will be made available:

  1. Didactic concept and MachineLearnAthon evaluation: A comprehensive framework and assessment methodology will be provided to support the educational aspects of MachineLearnAthon.
  2. 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.
  3. 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.
  4. 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.
  5. 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.