Kalman Filter 2: EKF (Extended Kalman), UKF (Unscented Kalman), Particle Filter (Particle Filter)

In-depth linear algebra for engineering majors in machine learning, AI, robotics, computer vision, and related fields. Recommended for those who want to study linear algebra in depth. The material is explained as simply as possible while covering advanced topics through focused selection of subjects.

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 operating principles of the Kalman Filter through easy examples.

In-depth linear algebra for engineering majors in machine learning, AI, robotics, computer vision, and related fields. Recommended for those who want to study linear algebra in depth. The material is explained as simply as possible while covering advanced topics through focused selection of subjects.

This course is a hands-on program for practicing the fundamentals of machine learning mathematics and statistics using Python. While machine learning and deep learning require programming skills, a mathematical background is essential to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence!

This course makes it easy to learn the essential mathematical concepts used in games. People who are already proficient in math often take this knowledge for granted, making it difficult for them to teach from a beginner's perspective. This course assumes you have no prior knowledge and explains everything from the viewpoint of a complete beginner or non-major.

A reintroduction to high school mathematics that starts with nothing more than your memory of middle school math. The course is reorganized according to the causal relationships between concepts rather than the textbook order, so you first understand why each formula is needed. Designed to be completed in 24 weeks with 30 minutes a day, it helps working professionals and adult learners build a solid foundation in the mathematical concepts they encounter at work and in everyday life.

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.