We are a group of students from the Thomas Jefferson High School for Science and Technology who are interested in machine learning. With help from Stanford ACM, we are proud to put on an annual Machine Learning competition open to high schoolers across the nation to futher their knowledge in Machine Learning and compete in a series of interesting, fun contests.

The National Machine Learning Open is an annual competition where high schoolers nationwide compete in a series of eight contests over the course of one week. The 2020 NMLO will run from August 17-23rd, 2020, beginning on midnight of the first day, ending at 11:59PM of the last (Eastern Standard Time).

There will be eight contests within the competition, varying in difficulty. Participants can choose to attempt the contests in any given order, and there is no restriction on which contests must be attempted/completed. Contest #1 will touch on very fundamental ML concepts, and the difficulty of the contests will progress to Contest #8.

Each of the contests in the 2020 NMLO will involve a different Machine Learning skill, and the competition will cover Machine Learning concepts/topics from simple neural networks, to more complex problems involving classification, language, images, videos, games, and more.

Anyone! While our competition is open to all participants who want to test out & further their Machine Learning knowledge and experience, the NMLO will only consider high school participants for judging eligibility.

We welcome participants with any amount of Machine Learning experience, from novices to more experienced Machine Learning practitioners. By RSVP-ing above, we will add you to a pre-competition week email list, where we will distribute a myriad of resources to help you become familiar with the Machine Learning concepts that will be covered in the NMLO.

We encourage collaborative learning prior to the beginning of competition week, and teams of up to two participants are encouraged to compete in the NMLO. Participants will have to ensure any work submitted is entirely their own.

This competition will be hosted virtually with the help of Kaggle InClass, where participants will be able to submit their code to evaluate accuracy and receive a score.

To be added to our email-list for competition participants (and if you want to be notified when the competition starts), RSVP!

Any further questions? Need help registering? Feel free to email us at tjmachinelearning@gmail.com, and we'll be sure to get back to you promptly.

Stanford ACM

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