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: Monday 3:00 pm - 5:00 pm, remote (Zoom)
Course Page: Blackboard & Course Website
Syllabus: View the syllabus here
Zoom: Meeting ID: 396 668 6420, Password: 587684
Class Schedule#
Section 03#
Midterm: The week starting Monday, February 17
Final Exam: The week starting Monday, April 21
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#
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 (01/06) |
Syllabus |
|
2 (01/13) |
Descriptive Statistics: slides | code | video |
|
3 (01/20) |
Data Cleaning & Integration: slides | video | code |
resources |
4 (01/27) |
Object Analysis: Similarity and distance measures: slides | video |
resources |
5 (02/03) |
Feature Analysis: Relationships: slides |
|
6 (02/10) |
Midterm Exam: topics | practice |
|
7 (02/17) |
Midterm Exam: questions |
resources |
8 (02/24) |
Feature Extraction: slides | code | video |
|
9 (03/03) |
Spring Break (No Class) |
resources |
10 (03/10) |
Decision Tree: slides |
resources |
11 (03/17) |
Classifier Evaluation, Model Selection: slides |
resources |
12 (03/24) |
Linear, Logistic regression and Perceptron: slides |
|
13 (03/31) |
||
14 (04/07) |
RNN: slides | video |
|
15 (04/14) |
resources |
|
16 (04/21) |
Final Exam |