Data Mining and Machine Learning Case Study
Chih-Jen Lin, Room 413, CSIE building.
- Time: Thursday 10:20am-1:10pm, Room 310, CSIE building.
Usually we have a 20-minute break at around 11:30am.
- This course will be taught in English.
- We don't assume that you know many machine learning or data
mining techniques. However, we do assume that you can quickly learn them (by yourself) when needed.
Usually in machine learning or data mining courses we learn many
classification, clustering, or data processing techniques. However,
there is no standard procedure on applying them to practical
applications. Very often a painful process of trial and error is
needed. While handling practical data may be an art but not science,
in this course, we try to gain experiences from some past and existing
data competitions: NIPS 2003 feature selection challenge, TRECVID
video concept detection, and Netflix movie recommendation competition.
Due to the instructor's background, we focus more on the use of
machine learning methods, but not designing features for various
applications. We will run this course in an interactive way, so
students must present their findings every week. We will accept no
more than 15 students.
In the beginning of each project, the instructor
gives a two-hour lecture introducing the
project and possible plans. We then spend the following
hour on dispatching jobs. In subsequent weeks, students
present their progress (15 minutes per student or group),
and we adjust our working plan.
If some techniques are needed and are new to most of us,
some of you may be asked to give short introductions.
It will be based on your results and
presentations every week.
Last modified: Fri Mar 9 06:06:32 CST 2007