While it is possible to learn a variety of classification, clustering and other mining techniques from lectures or books, applying them efficiently and accurately to the real-world data is a completely different story. Very often a painful process of trial and error is needed. While dealing with the practical issues on data is rather an art than science, in this course, we try to gain experiences from tackling some real-world problems proposed as the past or ongoing competitions in machine learning or data mining society. In particular, we aim at attending the ACM KDD CUP, which is currently the most prestigious data mining competition. We expect to run this course in an interactive way, so students must discuss with the lecturers and other classmates about their findings as well as the problems they encountered every week.