Topic outline

  • Welcome to Data Mining and Machine Learning


    Instructor: Md. Firoz Hasan
    Office : Room # 735, Level 7,Academic Building -4
    Cellphone #: 01705726026
    Email: firoz.cse@diu.edu.bd



    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%


    • 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

  • 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 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: Classification and Prediction: Alternative Techniques

    Topics of Discussion

    • Nearest-neighbor classifiers
    • Bayesian classifiers

  • Week 7: Mid Examination

    Midterm Examination Week



    • Week 8: Presentation

      Presentation Design Guide: How to Summarize Information for Presentations - Venngage

      • 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 12: Artificial Neural Networks

        Topics of Discussion

        • Neural Network
        • Application of neural network

      • Week 13: Review Week

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

        • Week 14: Final Examination


          Topics to be included in final exam:

          • Association rule mining
          • Clustering and applications
          • Nearest-Neighbor Classifier and Bayesian Classifier