Kalman Filter: Understanding the Mathematical Principles of State Estimation through Examples

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.

A coding test lecture taught by a world competition finalist! Now, stop solving by intuition and start solving with confidence!

We will make algorithms easy in just 2 weeks of lectures. We use computers for convenience, so why do you spend so much time on computers?

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.

This course provides an interactive introduction to discrete mathematics for computer science and engineering. Learners will study fundamental mathematical concepts, discrete structures, and discrete probability theory, developing the ability to apply these to various fields of computer science.

This is a lecture on [Data Structure and Algorithm Core Theory & Related Practical Problem Solving] for IT company coding test preparation. - Python version -

This is a hands-on course where you'll build an AI-based autonomous driving car using Raspberry Pi and various sensors.

Learn Python from scratch by fully utilizing ChatGPT. Rather than tedious conceptual explanations, the focus is on students directly experiencing coding and developing problem-solving skills through example problems and practical exercises. After mastering Python's basic syntax, you can build your skills by solving various problems in Level 1 and Level 2. Furthermore, you can identify and supplement your weaknesses through an Exam structured like a real test. By regularly solving problems and utilizing ChatGPT, students can have a memorable learning experience without feeling bored.

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.

This course is for those preparing for the CosPro Level 1 exam, covering the COS Pro Level 1 sample problems released by the exam organizer, YBM Net Co., Ltd.

RUR-PLE is an educational software created to help students learn software development more easily using Python. Here, you will learn what can be done with software and develop computational thinking skills through simple hands-on exercises.

The Code Puzzle Clear Challenge is a 4-week intensive training course based on "Learn to Code 1" in Swift Playgrounds, designed to develop "computational thinking" by giving commands to make a computer move as intended. By solving puzzle missions in each session, participants naturally learn to break down problems into smaller steps (decomposition), recognize patterns to organize repetitive rules using for-loops or functions, and make logical judgments to change actions based on specific situations. Beyond simply finding the right answer, participants reach a level where they can explain and refine "why they solved it this way."