Data Mining and Machine Learning
Instructor:
Chih-Jen Lin
, Room 413, CSIE building.
TA: Ming-Wei Chang, b6506056@csie.ntu.edu.tw
BBS: CS_DM2001 in ptt.csie.ntu.edu.tw
Time: Monday 9:10am-12pm, Room 409, CSIE building.
If there are too many students, we will move to a bigger room.
Textbook:
Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
by Ian H. Witten and Eibe Frank.
Lecture slides: downloadable from the
Morgan Kaufmann
web site
See the item "PDF slides" of the teaching material
SVM slides used can be downloaded
here
Reference:
Machine Learning
, Tom Mitchell, McGraw Hill, 1997.
Course Outline (tentative)
Input: Concepts, Instances, Attributes
Output: Knowledge Representation
Algorithms: The Basic Methods
Credibility: Evaluating What's Been Learned
Implementations: Real Machine Learning Schemes
Engineering the Input and Output
Machine Learning Algorithms In Java
Homework
Once every two weeks. Please write your homework/reports in English.
For late homework, the score will be exponentially decreased.
Please print out your homework but not e-mail it to the TA.
Homework 1
, due: October 18, 2001.
Homework 2
, due: October 29, 2001.
Homework 3
, due: November 19, 2001.
Homework 4
, due: December 24, 2001.
Exams
Midterm: November 19, 2001
Final: January 14, 2002
Final Projects
No final project.
Grading
50% homework, 50% Exam. (tentative); or
30% homework, 30% project, 40% Exam. (tentative)
Final grade
Related Information
Kdnuggets
: a useful collection of data mining related software, book, and many other stuff.
Last modified: Mon Jan 21 12:07:50 CST 2002