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.
Prerequisites: Check the respective program.
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 (9/3)
Chapter 1 & 2, Introduction
2 (9/10)
Chap 3. and Weka
3 (9/17)
Chap 4.1~4.4 Decision Trees
4 (9/24)
Chap 4.5 Association Rules
5 (10/1)
Chap 6.6 Clustering
6 (10/8)
Classifier IB
7 (10/15)
Review
8 (10/26)
Exam
9 (10/27~12/19)
A research paper due
Evaluation:
Exams (40%): There will be one online exam at the end of October.
Participation (30%): You must attempt all quizzes and participate a gropu discussion.
Report (30%): You must submit a research paper at the end of the semester.