Data Mining and Machine Learning: Theory and Practice, 2013
Our team from National Taiwan University wins KDD cup 2013
See the competition results: track 1,
track 2.
Our papers: track1, track2, talk slides at KDD cup workshop
Important announcements to potential students
In the past three years the focus of this course
was on KDD cup. However, for the next course
we may consider different settings.
In particular, we may arrange student
groups to attend several competitions organized by
different places.
We haven't made the decision yet but please keep in mind that we
may not participate at KDD cup 2013.
Please note that the course load is still similar.
You are expected to spend at least 10 hours per week
on this course.
Course Details
- Instructors:
Chih-Jen Lin, Hsuan-Tien Lin, and
Shou-De Lin
- TA: Cheng-Hao Tsai (r01922025 at csie.ntu.edu.tw),
Chun-Liang Li (macacaxdrz at gmail.com),
Ting-Wei Lin (b97083 at gmail.com)
-
course wiki is here
- Time: 9:10-12:00, Room 101, CSIE building.
We have a 20-minute break at around 10:30.
-
FAQ is here.
Note that this course
particularly aims to attend some data mining
competitions.
Course Outline (similar to past three years)
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. 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 Format (tentative)
More details will be on the wiki.
Exams
No exams
Grading
It will be based on your results and
presentations every week.
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