|
Weeks
|
Topic
|
Chapters
|
Assignments Due
|
|
1
|
Introduction:
Data Mining
and
Knowledge Discovery
|
Slides
|
|
|
2
|
What's
it all about? - Simple Examples
and Fielded Applications
|
1
|
|
|
3
|
Input:
Concepts, Instances and Attributes
|
2
|
|
|
4
|
Output:
Knowledge Representation
|
3
|
|
|
5
|
Algorithms: The
Basic Methods
- One Rule
The WEKA Data
Mining Workbench:
Weka Lab.
|
4 and
10~11
|
|
|
6
|
Algorithms:
The Basic Methods
- Statistical Modeling
|
4.2
|
|
|
7
|
Algorithms:
The Basic Methods
- Statistical Modeling
and Review for Mid-Term Exam
|
4.2
|
|
|
8
|
Mid-term
Exam
|
|
|
|
9
|
Algorithms:
The Basic Methods
- Decision Trees
|
4, 4.3 and 6, 6.1
|
|
|
10
|
Algorithms:
The Basic Methods
- Covering algorithms and Association rules
|
4, 4.4 and 4.5
|
Project
Guideline
|
|
11
|
Algorithms:
The Basic Methods
- Linear Models, Instance-Based Learning,
|
4, 4.6~4.7
|
|
|
12
|
Project
Proposal
|
Presentations
|
|
|
13
|
Algorithms:
The Basic Methods
- Clustering
The WEKA Data
Mining Workbench:
Weka Lab.
|
4.8
|
|
|
14
|
Credibility:
Evaluating What’s Been
Learned
|
5
|
|
|
15
|
Project
Presentations and Review
|
Presentations
|
|
|
16
|
Final
Exam
|
|
|