Welcome Message From Your Teacher
Course Rationale
An introduction to data mining; Data preparation, model building, and data mining techniques such as clustering, decisions trees and neural networks; Induction of predictive models from data:
classification, regression, and probability estimation; Application case studies; Data-mining software tools review and comparison.
Course Objectives
- To apply the concept of data mining in solving problems
- To demonstrate applications of data mining using tools
- To apply knowledge of data mining in project work
Course Outcomes (CO’s)
- CO1 Able to possess the basic knowledge of Weka and Python concerning data mining and machine learning
- CO2 Able to implement different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining to solve real-world problems using Weka and Python
- CO3 Able to compare and evaluate different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining using Weka and/or Python
- CO4 Able to apply implementation knowledge of data mining and machine learning in developing research ideas
Grading Scheme
Attendance: 10% Lab Performance: 25% Project / Lab Report: 25% Final Exam: 40%
|
- Introduction to Data Mining and Applications
- Data Mining Concepts and Techniques
- Data Mining Techniques
- Data Mining Using Weka
- Weka Manual
- Data Mining Using Python
- Global Data Repository for Data Mining and/or Machine Learning
- WISDM
- UCI ML Repository
- KDD Cup
- Kaggle
- KDnuggets
- IEEE Template
- ACM Template