Breakout Mentors offers the most hands-on way to learn Machine Learning for high school students. Machine learning (ML) is the study of techniques to solve complex tasks using data. Unlike traditional software, ML algorithms can adapt and improve their performance, and have become increasingly important in today’s world of big data. These algorithms power many of today’s applications, from search engines to speech recognition, disease detection, and semi-autonomous vehicles.
Our students learn how to solve real-world problems using modern tools and machine learning algorithms. Each student is carefully matched with an exciting mentor who will customize the project-based learning, solving problems that interest them from day one.
Our curriculum: example student projects
Students work on real-world projects that use datasets based on their interests. Within a few months they’ll be amazed at what they can accomplish. Here are just a few of the projects students have made with their mentor:
Automatic Handwritten Character Recognition — automatically recognize handwritten digits and letters
Generative Irish Folk Music Maker — generate Irish folk music based on thousands of examples
Automatic Captcha Solver — solve online captchas that attempt to tell humans from computers
Picasso Style Generator — convert an image into a painting in Picasso’s style
Pet Detector — tell whether an image contains a cat or a dog
Is this right for me?
It’s ok if you don’t have an extensive data background — we only require 6 months of coding experience and comfort with algebra. The field of data science is distinct enough from high school computer science and mathematics that we’ll cover what beginners need to know.
We’ve also had great success with advanced students. Once the student is familiar with the foundations of ML, we focus on Capstone Projects. These projects, chosen by the student, are ambitious and can take a month or more to complete. Capstone projects are an excellent way for students to apply their knowledge to active research problems in ML and are a great addition to their college applications or resume.
Our philosophy: teach the whole game and learn by doing
Many ML courses begin by asking students to trudge through definitions of complex theorems and dense mathematics without touching actual working code. These resources teach disconnected fundamentals and promise that they will pay off later, long after students have lost interest in the subject.
We take a different and unique approach: teach the whole game. That means that if we were teaching basketball, we first take the student to a basketball game or get them to play it. We don’t start by teaching them Newton’s Laws of Motion or the coefficient of restitution of a bouncing ball.
That’s why, starting from the first lesson, we get students to play with and build ML applications and challenge them to make these applications better. This approach encourages students to experiment, get creative, and gradually learn all the theoretical foundations they need, in context, so that they can see why it matters and how it works.
To support students, our ML mentors follow these principles:
- Teach the whole game. We’ll start by showing how to use a complete, deep learning network to solve real-world problems using modern tools. Then, we’ll progressively dig deeper and deeper into understanding how those tools are made, and how the tools that make those tools are made, and so on…
- Always teach through examples. We’ll introduce new topics in a context that students can intuitively understand and provide code they can play with rather than present complex algebraic formulas and technical jargon.
- Remove barriers. Until recently, ML has been a very exclusive game that required lots of resources ($$$) to play. We’ll show students how to utilize available resources to bring their ideas to life for free!
- Learn by doing. Whether students are excited to diagnose disease from x-rays, predict rainfall from weather patterns, auto-generate music or paintings, or determine what animals the dog barks at when nobody is home, we enable students to learn by building ML applications to solve their own problems.
Logistics that work for you
The weekly session time and location will fit conveniently into your schedule, taken into account when pairing your son or daughter with the perfect mentor. The majority of sessions are held online, but you can also meet in-person in the San Francisco Bay Area (students travel to the college campus for an empowering learning experience or in-home may be available for an extra-charge).