2024-Fall-CSCE566-Data Mining

Class Time: Mondays and Wednesdays, 2:30PM to 3:45PM.
Room: James R. Oliver, Room 119A.

Overview

This course covers fundamental methods and techniques for analyzing and mining real-world data, including topics such as big data analysis, classification, clustering, association rule mining, and representation learning.

Learning Objectives

• Understand basic concepts and techniques of data mining.
• Perform data preprocessing such as data cleaning, normalization, transformation, and dimensionality reduction.
• Analyze data using various supervised and unsupervised learning algorithms.
• Apply state-of-the-art data mining technology to real-world applications.

Prerequisites

CMPS 460, or consent with instructor

Instructor and Office Hours

Lecture Schedule

Textbook

This course does not have a required textbook. All necessary readings and materials will be made available on the course website.

Interested learners are recommended to the following texts:

• Jiawei Han, Jian Pei, Hanghang Tong. Data Mining Concepts and Techniques. 4th edition. Morgan Kaufmann, 2023.
• Wes McKinneyz. Python for Data Analysis, 3E. O’Reilly, 2023.
• Peter Bruce, Andrew Bruce, Peter Gedeck. Practical Statistics for Data Scientists . O’Rielly, 2016.
• James Garth, Witten Daniela, Hastie Trevor, Tibshirani Robert. An Introduction to Statistical Learning . Springer, 2021/2023.
• Tan P., Steinbach M., Kumar V., Introduction to Data Mining , 2ed., Pearson, 2018, ISBN 978-0133128901

Course Grading Scale

Course Evaluation Method

Homework (30%)

• HW1.
  • Due:
• HW2.
  • Due:

HW Note: All HWs due by the end of the day, Central Time.

Reading Assignment (30%)

TBD

Project (30%)

TBD