IE 7275. Data Mining in Engineering (4 Hours)

Course Outline:

1. Introduction to Data Mining

• • Fundamental concepts and key principles
  • Applications of data mining in engineering

1. Data Mining Techniques

• • Preprocessing techniques for data mining
  • Association rule extraction and its applications

1. Classification and Prediction

• • Supervised learning techniques in data mining
  • Building predictive models for engineering applications

1. Clustering

• • Unsupervised learning techniques in data mining
  • Grouping similar data points for analysis

1. Complex Data Exploration

• • Advanced data exploration techniques
  • Applications in manufacturing, healthcare, business, and services sectors

1. Data Mining Applications

• • Case studies in manufacturing, healthcare, medicine, and business
  • Ethical considerations in data mining applications

Assignments and Assessments:

• Data mining projects
• Classification and prediction models
• Clustering analysis assignments
• Midterm exam
• Final project: Applying data mining techniques to a real-world engineering dataset

Lesson Summary

Course Name: IE 7275 - Data Mining in Engineering (4 Hours)

Course Outline:

• Introduction to Data Mining
• Fundamental concepts and key principles
• Applications of data mining in engineering
• Data Mining Techniques
• Preprocessing techniques for data mining
• Association rule extraction and its applications
• Classification and Prediction
• Supervised learning techniques in data mining
• Building predictive models for engineering applications
• Clustering
• Unsupervised learning techniques in data mining
• Grouping similar data points for analysis
• Complex Data Exploration
• Advanced data exploration techniques
• Applications in manufacturing, healthcare, business, and services sectors
• Data Mining Applications
• Case studies in manufacturing, healthcare, medicine, and business
• Ethical considerations in data mining applications

Assignments and Assessments:

• Data mining projects
• Classification and prediction models
• Clustering analysis assignments
• Midterm exam
• Final project: Applying data mining techniques to a real-world engineering dataset