Welcome#
to our Knowledge Discovery and Data mining(KDD) course. In this course, we’ll delve into the world of data mining, uncovering valuable insights from vast datasets. Explore techniques for identifying meaningful patterns, correlations, and trends, and apply them to real-world and synthetic data. Topics encompass all stages of knowledge discovery, from association rules to cluster analysis, classification, and regression. Through hands-on coding, students will implement essential data mining algorithms and use existing tools to expand their skill set in practical applications
Course Information#
Instructor: Dr. Yong Zhuang
E-mail: yong.zhuang@gvsu.edu
Office: MAK D-2-234
Office Hours: Tuesday 3:00 pm - 5:00 pm, MAK D-2-234
Course Page: Blackboard & Course Website
Syllabus: View the syllabus here
Zoom: Meeting ID: 396 668 6420, Password: 587684
Class Schedule#
Section 01#
Class Time: Tuesday 6:00 pm - 8:50 pm
Room: Pew Campus | DeVos Center | Room 210A
Midterm: October 7 (Tuesday), 6:00 pm - 7:15 pm
Final Exam: December 9 (Tuesday), 6:00 pm - 7:50 pm
Section 02#
Class Time: Monday, Wednesday 4:30 pm - 5:45 pm
Room: Pew Campus | DeVos Center | Room 210A
Midterm: October 6 (Monday), 4:30 pm - 5:45 pm
Final Exam: December 10 (Wednesday), 4:00 pm - 5:50 pm
Preference Books#
There is no main textbook for the class. However, you may use materials from the following books as a reference. Lecture slides and additional reading materials will be provided on the class website.
Data Mining Concepts and Techniques (4th Edition) by Jiawei Han, Jian Pei, and Hanghang Tong. Publication Date: 2023. (free at GVSU library)
Tentative Schedule#
August 31 - September 1, 2025 Labor Day Recess: No classes!
October 19-21, 2025 Fall Break: No classes!
November 26-30, 2025 Thanksgiving Recess: No classes!
To execute the sample Jupyter Notebook code , click on the rocket icon at the top of the page, which will open the notebook in Google Colab for interactive use.
Week |
Content |
Reading |
|---|---|---|
1. 08/25 |
Syllabus |
|
2. 09/01 |
Descriptive Statistics: slides | code |
|
3. 09/08 |
Data Cleaning & Integration: slides | code |
resources |
4. 09/15 |
Similarity and Distance Measures: slides |
|
5. 09/22 |
Feature Analysis: Relationships: slides |
|
6. 09/29 |
resources |
|
7. 10/06 |
Midterm Exam |
|
8. 10/13 |
Feature Extraction, Feature Selection, Markov Blanket |
resources |
9. 10/20 |
Fall Break (No Class for Section 1), TBD for section 2 |
resources |
10. 10/27 |
Decision Tree |
resources |
11. 11/03 |
Classifier Evaluation, Model Selection, Bayesian Classification |
resources |
12. 11/10 |
Linear/Logistic Regression, Perceptron, Lazy Learning, Clustering |
resources |
13. 11/17 |
Neural Network, CNN |
resources |
14. 11/24 |
RNN, Attention, Transformer |
resources |
15. 12/01 |
Project Presentation, Final Exam Topics and Practice |
resources |
16. 12/08 |
Final Exam |