This is an in-depth linear algebra course for engineering majors in fields such as Machine Learning, AI, Robotics, and Computer Vision. It is recommended for those who wish to study linear algebra in depth. While explaining as simply as possible, it covers advanced topics through selective focus and concentration.

Acquiring probability theory is essential in the study of AI and machine learning. I recommend this to those who wish to study probability theory in more depth.

The core of all AI/deep learning is ultimately optimization. Of course, optimization theory is not only needed in AI/deep learning. Computer vision, robotics & control systems, financial engineering & quant, data science, signal processing... Probably no matter which field you study, optimization theory is one of the topics you will inevitably encounter when you go deeper. This lecture systematically covers everything from basic mathematical theory to in-depth content. I believe it is by far the best optimization theory lecture offered in Korea.

This is optimization theory required for AI/deep learning, computer vision, computer graphics, and more. Optimization Theory 1 focuses primarily on the definition of multivariable functions and their derivatives. Why is that? It is because all optimization problems are expressed in the form of multivariable functions. If you acquire an accurate definition of multivariable functions and the concept of differentiation, the theoretical approach to the aforementioned fields will become significantly easier.

Master the linear algebra content required for AI/deep learning by investing 10 minutes a day and completing it in one month.

You can understand the theoretical operation principle of the Kalman Filter through easy examples.