Creating a maths-based Open source syllabus for learning AI for the next generation

How can we bridge the gap between maths needed for AI and Machine learning with that taught in high schools (up to ages 17/18)?
To put this idea into some more context: The maths behind Machine Learning comprises of four key areas:
  • Linear algebra
  • Statistics and Probability theory
  • Multivariate calculus
  • Optimization
How can we start with high school maths and use that knowledge to bridge the gap with maths for AI and Machine Learning? Knowledge of maths is universal.
It means, we could truly inspire someone with minimal resources to take up Data Science as a profession.
As an educator, the main problem in teaching the maths behind Data Science is:
  • Cognitive dependencies: There are many inter-dependent concepts and to explain something new, you need to explain the dependencies which can be numerous
  • Cognitive overload: There are too many things to learn in a short timeframe. Related to this, is the fact that there is too much content out there. While, the content on the Web is excellent in many cases, it can be overwhelming
Hence, a simplified approach is needed to learn the mathematics behind Data Science and Artificial Intelligence.
We are addressing this problem by working with teachers globally.
We are creating a maths-based Open source syllabus for learning AI for the next generation.
More details coming soon...
Please contact ajit.jaokar at for more information. The name feynlabs is inspired by the vision of Richard Feynman as a systems thinker, teacher and a humanist.
Contact ajit.jaokar at