Welcome to your go-to resource for diving deeper into the Math of Machine Learning! This guide is curated for both high schoolers and those who might find their mathematical skills a bit rusty. Here’s how you can systematically approach the mathematics necessary for mastering Machine Learning.

Why Mathematics is Important in Machine Learning?

Mathematics is the foundation upon which Machine Learning is built. Understanding concepts like algebra, calculus, and statistics enables us to design, analyze, and improve algorithms effectively. To get a deeper understanding, watch this insightful video where Siraj Raval explains the four essential math disciplines involved in the field of Machine Learning.

How to Use This Resource List

The resources below are organized by topics, starting with foundational knowledge and moving on to more advanced concepts. Let’s break it down!

The High Schoolers Guide to the Math of Intelligence

Prerequisite Knowledge:

• Learn Algebra
  • Algebra I – Khan Academy
  • Algebra II – Khan Academy

Linear Algebra:

Books:

• The Manga Guide to Linear Algebra – An enjoyable introduction to Linear Algebra through a manga format.
• Introduction to Linear Algebra – Gilbert Strang – A more detailed textbook, recommended for use alongside the MIT OCW course.
• Linear Algebra: A Modern Introduction – A user-friendly alternative to Strang’s book.

MOOCs:

• Essence of Linear Algebra – 3Blue1Brown
• Linear Algebra – Khan Academy
• Linear Algebra – Gilbert Strang (MIT OCW)

Calculus:

Books:

• The Manga Guide to Calculus – An engaging comic-style guide to Calculus.

MOOCs:

• Essence of Calculus – 3Blue1Brown
• Differential Calculus – Khan Academy
• Multivariable Calculus – Khan Academy

Statistics & Probability:

MOOCs:

• Statistics and Probability – Khan Academy
• Introduction to Probability – MIT

Books:

• Think Stats – Allen Downey
• The Manga Guide to Statistics

Further Reading for Confident Learners

If you feel confident with the Math of Intelligence, explore these research papers:

• The Matrix Calculus You Need for Deep Learning – Terence Parr and Jeremy Howard
• (Printable Version)

Weekly Structure

To help organize your learning, we have created a Trello board to guide you through this curriculum on a weekly basis.

Troubleshooting Ideas & Instructions

If you encounter any difficulty while navigating through these resources, here are a few suggestions:

• Take your time with each concept; mathematics requires practice.
• Form study groups with peers to discuss and solve problems collaboratively.
• Utilize online forums and discussion boards for additional support.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.