How To Learn the Math of AI For FREE in 2020

TL;DR

An open-source course plan is provided to learn the mathematics of AI, covering calculus, linear algebra, differential equations, computational thinking, and computer science fundamentals.

Transcript

what's up everybody in today's video you are gonna learn an open-source course plan to learn the mathematics of artificial intelligence no hype no BS just cold hard truth let's get started so this is motivated by a question have you ever asked me on my video on how to learn AI for free and I think this is a really great question and something I can... Read More

Key Insights

• 🎵 MIT OpenCourseWare provides comprehensive resources, including lectures, notes, exams, and assignments, for learning AI-related mathematics.
• ❓ Calculus, linear algebra, and differential equations are foundational topics necessary for understanding AI concepts like gradient descent and matrix operations.
• 🤔 Computational thinking and data science courses enhance understanding of optimization problems, graph models, and machine learning algorithms.
• ✋ Knowledge of computer science fundamentals, including software construction and programming paradigms, ensures the development of high-quality AI code.

Questions & Answers

Q: Why is calculus important for learning AI?

Calculus is crucial for understanding concepts like gradient descent in AI, which rely on multi-dimensional derivatives. It also helps with understanding vector theorems and high-dimensional spaces.

Q: How important is it to do extra problem sets while learning mathematics?

Doing a large quantity of problems is highly recommended as it strengthens your understanding and mathematical skills. The more repetitions you do, the better you'll get at solving mathematical problems.

Q: Is it necessary to learn differential equations for AI?

While not strictly necessary, having a solid understanding of differential equations can be valuable for a deeper understanding of AI concepts. It helps with solving equations related to calculus and physics.

Q: Why is learning computer science fundamentals important for AI?

Understanding software construction, concurrent programming, and design patterns is crucial for writing bug-free and easily understandable code in AI. It ensures that the intent of the algorithm is clear and adaptable.

Summary & Key Takeaways

• Majority of viewers are college-aged, so college students should follow their recommended course sequence.

• For those out of college and needing a refresher, MIT OpenCourseWare provides lectures, notes, assignments, and exams with real-time feedback for learning AI.

• The course plan includes single variable calculus, multivariate calculus, linear algebra, computational thinking, data science, and software construction.