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Topic outline
- Welcome to Data Mining
Welcome to Data Mining
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%
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- 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
- Week 1: Introduction to Weka
Week 1: Introduction to Weka
Topics of Discussion
- Introduction to Weka
- Relationship to data mining
- Overview of data mining with Weka
Expected Learning Outcome
- Appreciation of the needs of data mining with Weka
- Visualization of the relationship of Weka to data mining
- Visualization of different data mining tasks with Weka
- Week 2: Data Visualization Using Weka
Week 2: Data Visualization Using Weka
Topics of Discussion
- Review of data mining task and related
application examples
- Data Visualization with Weka
- Course Project
Team and discussion
Expected Learning Outcome
- On-hand acquaintance and practice of data visualization with Weka
- Team formation for the course project
- Week 3: Feature/Attribute Selection Using Weka
Week 3: Feature/Attribute Selection Using Weka
Topics of Discussion
- Discussion on feature/attribute selection
- Project Discussion
and execution plan
Expected Learning Outcome
- On-hand acquaintance and practice of feature/attribute selection with Weka
- Selection of
project topic by team
- Week 4: Classification Using Weka
Week 4: Classification Using Weka
Topics of Discussion
- Classification and prediction with Weka
- Classification: decision tree
Expected Learning Outcome
- Problem solving skill in classification and prediction
- Skill in using Weka as a data mining tool for classification and prediction
- Week 5: Continuation of Classification Using Weka
Week 5: Continuation of Classification Using Weka
Topics of Discussion
- Classification and prediction with Weka
- Classification: Bayesian, Instance-based
Expected Learning Outcome
- Problem solving skill in classification and prediction
- Skill in using Weka as a data mining tool for classification and prediction
- Week 6: Cluster Analysis Using Weka
Week 6: Cluster Analysis Using Weka
Topics of Discussion
- Cluster Analysis with Weka
- Cluster Analysis: partitional (K-means), hierarchical, density-based
Expected Learning Outcome
- Problem solving skill in classification and prediction
- Skill in using Weka as a data mining tool for cluster analysis
- Week 7: Mid Exam
Week 7: Mid Exam
Midterm Examination Week
- Week 8: Presentation of Project # 1 (Using Weka)
Week 8: Presentation of Project # 1 (Using Weka)
Project # 1 (with Weka) Presentation
- Week 9: Introduction to Python
Week 9: Introduction to Python
Topics of Discussion
- Introduction to Python
- Relationship to machine learning
- Overview of machine learning with Python
Expected Learning Outcome
- Appreciation of the needs of machine learning with Python
- Visualization of the relationship of Python to machine learning
- Visualization of different machine learning tasks with Python
- Week 10: Classification Using Python
Week 10: Classification Using Python
Topics of Discussion
- Classification and prediction with Python
- Classification: decision tree
Expected Learning Outcome
- Problem solving skill in classification and prediction
- Skill in using Weka as a data mining tool for classification and prediction
- Week 11: Classification Using Python (Continued)
Week 11: Classification Using Python (Continued)
Topics of Discussion
- Classification and prediction with Python
- Classification: decision tree
Expected Learning Outcome
- Problem solving skill in classification and prediction
- Skill in using Weka as a data mining tool for classification and prediction
- Week 12: Cluster Analysis Using Python
Week 12: Cluster Analysis Using Python
Topics of Discussion
- Cluster Analysis with Python
- Cluster Analysis: partitional (K-means), hierarchical, density-based
Expected Learning Outcome
- Problem solving skill in classification and prediction
- Skill in using Weka as a data mining tool for cluster analysis
- Ability to apply data mining knowledge in development project
- Week 13: Presentation of Project # 1 (Using Python)
Week 13: Presentation of Project # 1 (Using Python)
Project # 2 (with Python) Presentation
- Week 14: Final Examination
Week 14: Final Examination
Topics to be included in final exam:
- Classification (with Weka and Python)
- Cluster Analysis (with Weka and Python)
Lab Final Section (G) Quiz
Restricted Not available unless: You belong to Section G
Lab Final Section (E) Quiz
Restricted Not available unless: You belong to Section E
Lab Final_(F) Quiz
Restricted Not available unless: You belong to Section F
Lab Final_(H) Quiz
Restricted Not available unless: You belong to Section H
- Topic 15
Topic 15
Lab Task (F) Assignment
Restricted Not available unless: You belong to Section F
Lab Task (E) Assignment
Restricted Not available unless: You belong to Section E
Lab Task (G) Assignment
Restricted Not available unless: You belong to Section G
Lab Task (H) Assignment
Restricted Not available unless: You belong to Section H
- Project Video Link Submission
Project Video Link Submission
- Topic 17
- Topic 18
- Topic 19
- Topic 20