I'm majoring in Artificial Intelligence, and I'm starting to wonder what kind of AI I was actually learning before. After learning the foundational mathematics, the theory became the basis, and I definitely understand the AI I learned much better and gained a deeper understanding. The content isn't too long either, so it's not boring, and it's a great help to go through it once.


This is the optimization theory required for AI/deep learning, computer vision, computer graphics, etc. Optimization Theory 1 mainly deals with the definition of multivariable functions and differentiation of multivariable functions. Why is that? Because all optimization problems are expressed in the form of multivariable functions. If you acquire the exact definition of multivariable functions and the concept of differentiation, the theoretical approach in the above fields will become considerably easier.

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Optimization theory 1: 10 minutes a day, 1 month completion

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optimization-problem

Linear Algebra

Machine Learning(ML)

Computer Vision(CV)

Introduction to Optimization Theory

Differentiation of multivariable functions

Taylor expansion of multivariable functions

Convex Function

Optimization Theory

Lagrange multiplier method