Textbook:
Ian H. Witten and Eibe Frank, Data Mining Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, (1999)
Description:
This course will provide an overview of topics such as introduction to data mining and knowledge discovery;
data mining with structured and unstructured data;
foundations of pattern clustering; clustering paradigms; clustering for data mining;
data mining using neural networks and genetic algorithms;
fast discovery of association rules; applications of data mining to pattern classification; and feature selection.
The goal of this course is to introduce students to current machine learning and related data mining methods. It is intended to provide enough background to allow students to apply machine learning and data mining techniques to learning problems in a variety of application areas. Course projects will be required.
Prerequisites: CS 623 or IS 613
Lecture Notes: can be accessed using the http://blackboard.pace.edu
Blackboard Login Procedures for Registered Students are available
here
Tentative Schedule:
Week
Topic
1 (1/24)
Chapter 1
2 (1/31)
Chap 2. Input: Concepts, instances, attributes
3 (2/7)
Chap 3. Output: Knowledge representation
4 (2/14)
Chap 4.1~4.4 Decision Trees
5 (2/21)
Chap 4.5 Association Rules
6 (2/28)
Chap 4.6~4.8
7 (3/7)
Chap 5.1 ~5.6
8 (3/14)
Chap 5.7 ~5.11
9 (3/28)
Chap 6.1~6.3
10 (4/4)
Chap 6.4~6.5
11 (4/11)
Chap 6.6 Clustering
12 (4/18)
Chap 7.1~7.2
13 (4/25)
Chap 7.3~7.5
14 (5/2)
Evaluation:
Online Quiz (50%): There will be 10 quizzes.
Participation (50%): You must post your opinion and responses
on the discussion board.