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Topic outline
- Welcome to Data Mining and Machine Learning
Welcome to Data Mining and Machine Learning
Instructor: Md. Aynul Hasan Nahid Office : Room # 712, Level 7, Daffodil Tower Cellphone #: 01674834062 Email: aynul.cse@diu.edu.bd
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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
appreciate the necessity of data mining in everyday life
- 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 grasp the basic Data Mining Principles
- CO2 Able to identify appropriate data mining algorithms to solve real world problems
- CO3 Able to compare and evaluate different data mining techniques like classification, prediction, clustering and association rule mining
- CO4 Able to apply data mining knowledge in problem solving
Grading Scheme
Attendance: 7% Class Tests/Quizzes: 15%
Assignment: 5% Presentation (using video/ppt): 8%
Midterm Exam: 25% Final Exam: 40%
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- Introduction to Data Mining by Tan
- Machine
Learning by Tom Mitchell
- Reference Reading Materials
- Introduction to Data Mining and Applications
- Data Mining Concepts and Techniques
- Data Mining Techniques
- IEEE Template
- ACM Template
- Week 1: Introduction
Week 1: Introduction
Topics of Discussion
- Introduction to data mining
- Relationship to data warehousing
- Why data mining is a discipline?
- Overview of data mining tasks: Clustering,
Classifications, Rules learning etc
Expected Learning Outcome
- Appreciation of the needs of data mining
- Visualization of data warehouse and relationship
to data mining
- Visualization of different data mining tasks
- Week 2: Differences between Data Mining and Machine Learning
Week 2: Differences between Data Mining and Machine Learning
- Week 3: Data
Week 3: Data
Topics of Discussion
- Review of data mining task and related
application examples
- Data Warehousing Introduction
- Week 4: Exploring Data
Week 4: Exploring Data
Topics of Discussion
- Discussion on data mining process: Data
preparation and cleansing and task identification
- Project Discussion
and execution plan
- Week 5: Classification and Prediction
Week 5: Classification and Prediction
Topics of Discussion
- Classification and Prediction
- Classification: tree-based approaches
- Week 6: Classification and Prediction: Alternative Techniques
Week 6: Classification and Prediction: Alternative Techniques
Topics of Discussion
- Nearest-neighbor classifiers
- Bayesian classifiers
- Week 7: Mid Examination
- Week 8: Presentation
- Week 9: Cluster Analysis
Week 9: Cluster Analysis
Topics of Discussion
- Clustering Data
- Problem Solving using Clustering
- Week 10: Cluster Analysis (Continued) and Association Rule Mining
Week 10: Cluster Analysis (Continued) and Association Rule Mining
Topics of Discussion
- Association Rule Mining
- Problem Solving using association rule mining
- Week 11: Association Rule Mining (Continued)
Week 11: Association Rule Mining (Continued)
Topics of Discussion
- Association Rule Mining
- Problem Solving using association rule mining
- Week 12: Artificial Neural Networks
Week 12: Artificial Neural Networks
Topics of Discussion
- Neural Network
- Application of neural network
- Week 13: Review Week
Week 13: Review Week
- Research article
writing, review and publishing
- Review discussion and advanced topics.
- Week 14: Final Examination
Week 14: Final Examination
Topics to be included in final exam:
- Association rule mining
- Clustering and applications
- Nearest-Neighbor Classifier and Bayesian Classifier