Kalman Filter 2: EKF (Extended Kalman), UKF (Unscented Kalman), Particle Filter (Particle Filter)
You can understand the theoretical operating principles of the EKF (Extended Kalman), UKF (Unscented Kalman), and Particle Filter through simple examples.
Intermediate
Python, Linear Algebra, kalman-filter
Kalman Filter 2: EKF (Extended Kalman), UKF (Unscented Kalman), Particle Filter (Particle Filter)
Linear Algebra: A lecture for engineering majors in machine learning, AI, robotics, computer vision, and related fields
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.
Basic
Python, Machine Learning(ML), Deep Learning(DL)
Linear Algebra: A lecture for engineering majors in machine learning, AI, robotics, computer vision, and related fields
Probability Theory for AI and Machine Learning: The Core of Bayes, Distributions, and Estimation
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.
Basic
Python
Probability Theory for AI and Machine Learning: The Core of Bayes, Distributions, and Estimation
Optimization Theory Bible
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.
Intermediate
optimization-problem
Optimization Theory Bible
Optimization Theory 1: Complete in One Month with 10 Minutes a Day
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.
Basic
optimization-problem, Linear Algebra, Machine Learning(ML)
Optimization Theory 1: Complete in One Month with 10 Minutes a Day
Linear Algebra 10 minutes a day, complete in a month
Master the linear algebra content required for AI/deep learning by investing 10 minutes a day and completing it in one month.
Basic
Linear Algebra, Procession, Deep Learning(DL)
Linear Algebra 10 minutes a day, complete in a month
Kalman Filter 1: A lecture explaining the exact mathematical principles of the Kalman filter through examples
You can understand the theoretical operating principles of the Kalman filter through easy examples.
Basic
Python, MATLAB, Linear Algebra
Kalman Filter 1: A lecture explaining the exact mathematical principles of the Kalman filter through examples
