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Topic outline
- CSE322 // Section L + M
CSE322 // Section L + M
Welcome message from Course Instructor
Instructor Name : Sharun Akter Khushbu
Designation : LecturerOffice Address : Department of CSE
Contact No : 01730599307Email : sharun.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
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
- Data Mining and Machine Learning Forum Lab Discussion
Data Mining and Machine Learning Forum Lab Discussion
- Week 1: Introduction to Python
Week 1: Introduction to Python
Topics of Discussion
- Introduction to Python
- Relationship to data mining
- Overview of Python basics
Expected Learning Outcome
- Appreciation of the needs of data mining with Python
- Visualization of different Basic Python tasks with Python
- Week 2: Data Visualization Using python
Week 2: Data Visualization Using python
Topics of Discussion
- Review of data mining task and related
application examples
- OOP with python and Function
- Data Visualization with python
- Course Project
Team and discussion
Expected Learning Outcome
- On-hand acquaintance and practice of data visualization with python
- Team formation for the course project
- Week 3: Numpy & Pandas Using Python
Week 3: Numpy & Pandas Using Python
- Week 4: Feature/Attribute Selection Using python
Week 4: Feature/Attribute Selection Using python
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 python
- Selection of
project topic by team
- Week 5: Classification Using python
Week 5: 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 python as a data mining tool for classification and prediction
- Week 6: Visualization Using Python
Week 6: Visualization Using Python
Topics of Discussion
- Data plotting with Matplotlib
- Visualization techniques
- Data Visualization with python
Expected Learning Outcome
- On-hand acquaintance and practice of data visualization with python
- Team formation for the course project
- Week 7: Continuation of Classification Using Python
Week 7: Continuation of Classification Using Python
Topics of Discussion
- Classification and prediction with python
- Classification: Bayesian, Instance-based
Expected Learning Outcome
- Problem solving skill in classification and prediction
- Skill in using python as a data mining tool for classification and prediction
- Week 8: Cluster Analysis Using Python
Week 8: Cluster Analysis Using Python
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 9: Introduction to Python in neural network
Week 9: Introduction to Python in neural network
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
- Week 8: Presentation of Project # 1 (Using Python)
Week 8: Presentation of Project # 1 (Using Python)
Project # 1 (with Python & ML) Presentation
- 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)