I applied because I wanted to systematically learn about recommendation systems, from basics to practical application. The lectures were systematically structured, covering everything from fundamental concepts of recommendation systems (content-based, collaborative filtering, etc.) to the latest deep learning-based methods. Practical coding exercises were also included, allowing me to grasp both theory and practice simultaneously. In particular, the process of directly implementing Matrix Factorization, LightFM, and deep learning-based recommendation models was very impressive, and the Kaggle practical examples were a great help for real-world applications. The instructor's explanations were clear, and the practice code was meticulously prepared, making it easy to follow along. I highly recommend this to those who are new to recommendation systems or those preparing for practical application!