Data Mining and Machine Learning: Theory and Practice, Spring 2012

Course Description

While it is possible to learn a variety of machine learning and data mining theories from lectures or books, applying them accurately and efficiently to the real-world data is a completely different story. Very often data miners have to suffer a painful process of trial and error due to lack of experience. Therefore, dealing with the practical issues on data is frequently viewed as art rather than as science.

In this course, we try to build up our experiences on the art by tackling real-world problems that appear the ongoing competitions in data mining society. In particular, we aim at attending ACM KDDCup 2012, which is currently the most prestigious data mining competition. We expect to run this course in an interactive way, in which students must discuss with the instructors and other classmates about their findings as well as the problems they encountered every week.


Course Information

Course Plan (Tentative)

details to come on the course wiki

FAQ (Modified from Last Year)