Topic outline

  • Welcome to Data Mining and Machine Learning


    Navigation 
    Class Test:  CT1 | CT2 | CT3      Assignment        Presentation        Mid Exam       Final Exam
    Week2Go:   WK1    WK2    WK3    WK4    WK5    WK6   WK7    WK8    WK9    WK10    WK11    WK12    WK13    WK14




    Course Instructor:

    Welcome message

    I'm Johora Akter Polin(JAP) and I will be your instructor for the course. I am looking forward to a terrific semester with you. I want to welcome everyone, and hope we will learn and grow together by the grace of Almighty Allah.



    • Text Book

    1. Introduction to Data Mining by Tan
    2. Machine Learning by Tom Mitchell
    • Reference Reading Materials
    1. Introduction to Data Mining and Applications
    2. Data Mining Concepts and Techniques
    3. Data Mining Techniques


    • Standard Templates
    1. IEEE Template
    2. ACM Template



    • Made with Padlet

    • Made with Padlet

  • 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


    Topic of Discussion:

    • Introduction to Data mining and Machine learning
    • Difference between Data Mining and ML
    • Classifications


  • Week 3: Data


    Topics of Discussion

    • Review of data mining task and related application examples
    • Data Warehousing Introduction


  • 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

    Topics of Discussion


    • Classification and Prediction
    • Classification: tree-based approaches
    		                                  


  • Week 6: Mid Examination

    MIDTERM Examination Summer 2021


    Date : 8/07/21               Time: 1:30 pm - 4:00 pm

    Data Mining and Machine Learning 

    CSE321


  • Week 7: Classification and Prediction: Alternative Techniques

    Topics of Discussion

    • Nearest-neighbor classifiers
    • Bayesian classifiers

  • Week 9: Cluster Analysis

    Topics of Discussion

    • Clustering Data
    • Problem Solving using Clustering

  • 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)

    Topics of Discussion

    • Association Rule Mining
    • Problem Solving using association rule mining


  • Week 13: Review Week

    • Research article writing, review and publishing
    • Review discussion and advanced topics.

    • Week 14: Final Examination


      Date: 29/08/21      Time: 1:30PM - 5:00 PM

      Data Mining and Machine Learning 
      CSE321