CS545 - ML for Data Mining / CS407 - Machine Learning

Summer 2024 (online asynchronous)

Classes: Online Asynchronous, May 28 - Jul 22, 2024
Instructor: Dr. Zdravko Markov, 30307 Maria Sanford Hall, (860)-832-2711, http://www.cs.ccsu.edu/~markov/, e-mail: markovz at ccsu dot edu
Office hours: TBA, via Blackboard Collaborate

Prerequisites by topic

Required Textbook

Ian H. Witten, Eibe Frank, and Mark A. Hall, Christopher Pal. Data Mining: Practical Machine Learning Tools and Techniques (Fourth Edition), Morgan Kaufmann, January 2017, ISBN 978-0-12-804291-5.

Required Software

The Weka Workbench - open-source machine learning software aailable at https://ml.cms.waikato.ac.nz/weka/index.html

Course Goals

Grading Policies

Grading will be based on eight assignments (75%), one quiz (10%) and class participation through scheduled discussions (15%). The letter grade will be determined by the following grading scale:
 
A
A-
B+
B
B-
C+
C
C-
D+
D
D-
F
94-100
90-93
87-89
84-86
80-83
77-79
74-76
70-73
67-69
64-66
60-63
0-59

Late assignments will be marked one letter grade down for each 3 days they are late. It is expected that all students will conduct themselves in an honest manner and NEVER claim work which is not their own. Violating this policy will result in a substantial grade penalty or a final grade of F.
 

Course Content (12 units)

  1. Introduction
  2. Data Warehouse and OLAP
  3. Data preprocessing
  4. Data mining knowledge representation
  5. Attribute-oriented analysis
  6. Learning Association rules
  7. Machine Learning algorithms: Classification
  8. Machine Learning algorithms: Prediction
  9. Evaluating what's been learned
  10. Mining real data
  11. Machine Learning algorithms: Clustering
  12. Text and Web Mining