Linear Algebra 10 minutes a day, complete in a month

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 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.

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

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.

Were you a math-phobe during your school days? It's said that math is important in AI, but do you feel lost on how to approach it? The reason math is important in AI is because math is used in the computational processes of AI (Machine Learning and Deep Learning). For easy explanations of differentiation, linear algebra, and probability and statistics to understand AI, it is structured with simple explanations using graphs (coordinates) rather than mathematical derivations.

This is a lecture where you can easily learn important mathematical concepts used in games. People who are well-versed in mathematics take this knowledge for granted, so they cannot teach it assuming you know nothing. I explain from a beginner's perspective, truly assuming you are a non-major.

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.