Topic outline

  • Welcome to Data Mining Course


     Welcome Note 

    Hello Students!

    Welcome to the world of "Data Mining" (CSE450) in Fall 2021. In this course, you are going to learn the fundamental concepts of Data Mining. You will get to know some basic tasks and algorithms which are related to data mining problems. We are going to study in a way that you will get all the support in this online platform. The course is designed with plenty of tutorials and resources. You will find course contents, reference books, course delivery plans, all kinds of announcements, and contact information here.

    So, let's start our journey and make this semester a great and remarkable one.



    Instructor's Information:      

     
     Instructor Name: Nadira Anjum Nipa
    Designation: Lecturer
    Email: nadira.cse@diu.edu.bd
    Office Address: CSE Faculty room, Level -6, House Building, Uttara Campus, Dhaka – 1230
     

    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 Learning Outcome: (at the end of the course, student will be able to do:)

    CLO1

    Able to conceptualize basic applications, concepts, and techniques of data mining

    CLO2

    Able to identify appropriate data mining algorithms to solve real world problems

    CLO3

    Able to compare and evaluate different data mining techniques like classification, prediction, clustering and association rule mining

    CLO4

    Able to apply knowledge of data mining in developing research ideas

    Grading Scheme
    Attendance: 7%
    Class Tests/Quizes:  15%
    Assignment: 5%
    Presentation (using video/ppt): 8%
    Midterm Exam: 25%
    Final Exam: 40%


    • Text Book

    1. Introduction to Data Mining by Tan
    • Reference Reading Materials
    1. Introduction to Data Mining and Applications
    2. Data Mining Concepts and Techniques
    3. Data Mining Techniques
    4. Data Mining using Weka
    5. Weka Manual
    6. Data Mining using Python
    • Global Data Repository for Data Mining
    1. WISDM
    2. UCI ML Repository
    3. KDD Cup
    4. Kaggle
    5. KDnuggets

    • Standard Templates
    1. IEEE Template
    2. ACM Template


    • OBE Course Outline

    • Class Schedule:

      • PC-B: Monday 10:00-11:30 am, Wednesday 10:00-11:30 am
      • PC-G: Tuesday  1:00-2:30 pm, Thursday 11:30-1.00 pm 
      • PC-H: Tuesday  10:00-11:30 am, Thursday 2:30-4.00 pm

      Telegram Link:
      • PC-B: https://t.me/joinchat/xaEqivqd58MzNWE1
      • PC-G: https://t.me/joinchat/AOCTm4j8MhViMzM1
      • PC-H: https://t.me/joinchat/rKXFoBXktpg0Njk9

      Google Meet Link:
      • PC-B: meet.google.com/smt-wxad-uco
      • PC-G: meet.google.com/qxp-ttfw-fvz
      • PC-H: meet.google.com/sps-hcmp-mrj

  • 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

    • Class-1 Lecture Video (PC-B) URL
      Restricted Not available unless: You belong to PC-B Fall 2021
    • Class-1 Lecture Video (PC-H) URL
      Restricted Not available unless: You belong to PC-H Fall 2021
    • Class-1 Lecture Video (PC-G) URL
      Restricted Not available unless: You belong to PC-G Fall 2021

  • Week 2: Working with Data

    Topics of Discussion

    • Review of data mining task and related application examples
    • Data Warehousing Introduction
    • Course Project Team and discussion


    Expected Learning Outcome

    • Identification of data mining task
    • Team formation for the course project

    • Class-1 Lecture Video (PC-B) URL
      Restricted Not available unless: You belong to PC-B Fall 2021
    • Class-1 Video Lecture (PC-H) URL
      Restricted Not available unless: You belong to PC-H Fall 2021
    • Class-1 Lecture Video (PC-G) URL
      Restricted Not available unless: You belong to PC-G Fall 2021
    • Class-2 Lecture Video (PC-B) URL
      Restricted Not available unless: You belong to PC-B Fall 2021
    • Class-2 Lecture Video (PC-G) URL
      Restricted Not available unless: You belong to PC-G Fall 2021
    • Class-2 Lecture Video (PC-H) URL
      Restricted Not available unless: You belong to PC-H Fall 2021

  • Week 3: Data Exploration

    Topics of Discussion

    • Discussion on data mining process: Data preparation and cleansing and task identification
    • Project Discussion and execution plan
    • Class Test # 1

    Expected Learning Outcome

    • Visualization data mining processes
    • Selection of project topic by team

    • Additional Reading

    • Class-1 Lecture Video (PC-B) URL
      Restricted Not available unless: You belong to PC-B Fall 2021
    • Class-1 Lecture Video (PC-H) URL
      Restricted Not available unless: You belong to PC-H Fall 2021
    • Class-1 Lecture Video (PC-G) URL
      Restricted Not available unless: You belong to PC-G Fall 2021
    • Class-2 Lecture Video (PC-B) URL
      Restricted Not available unless: You belong to PC-B Fall 2021
    • Class-2 Lecture Video (PC-G) URL
      Restricted Not available unless: You belong to PC-G Fall 2021
    • Class-2 Lecture Video (PC-H) URL
      Restricted Not available unless: You belong to PC-H Fall 2021

    • ..................................................................

      QUIZ-1

      ..................................................................


      Syllabus: 

      • Wk1-Introduction 
      • Wk2-About Data. 

      Question pattern: MCQ and Written.

    • Class Test-1 (PC-B) Quiz
      Restricted Not available unless: You belong to PC-B Fall 2021
    • Class Test-1 (PC-G) Quiz
      Restricted Not available unless: You belong to PC-G Fall 2021
    • Class Test-1 (PC-H) Quiz
      Restricted Not available unless: You belong to PC-H Fall 2021

  • Week 4: Classification and Prediction

    Topics of Discussion

    • Classification and Prediction
    • Classification: tree-based approaches

    Expected Learning Outcome

    • Problem solving for classification and prediction
    • Using Weka and other DM tools

    • Slide Decision Tree

    • Class-1 Lecture Video (PC-G) URL
      Restricted Not available unless: You belong to PC-G Fall 2021
    • Class-1 Lecture Video (PC-H) URL
      Restricted Not available unless: You belong to PC-H Fall 2021
    • Class-1 Lecture Video (PC-B) URL
      Restricted Not available unless: You belong to PC-B Fall 2021
    • Class-2 Lecture Video (PC-H) URL
      Restricted Not available unless: You belong to PC-H Fall 2021
    • Class-2 Lecture Video (PC-G) URL
      Restricted Not available unless: You belong to PC-G Fall 2021
    • Class-2 Lecture Video (PC-B) URL
      Restricted Not available unless: You belong to PC-B Fall 2021

  • Week 5: Classification Tuning

    Topics of Discussion

    • Classification and Prediction with tuning
    • Classification: tree-based approaches
    • Class Test # 2

    Expected Learning Outcome

    • Problem-solving for classification and prediction
    • Using Weka and other DM tools

    • Gini Index File
    • Class-1 Lecture Video on DT-Part2 (PC-H) URL
      Restricted Not available unless: You belong to PC-H Fall 2021
    • Class-1 Lecture Video on DT-Part2 (PC-G) URL
      Restricted Not available unless: You belong to PC-G Fall 2021
    • Class-1 Lecture Video DT-Part2 (PC-B) URL
      Restricted Not available unless: You belong to PC-B Fall 2021

    • ..................................................................

      QUIZ-2

      ..................................................................


      Syllabus: 

      • Wk3-DataExploration
      • Wk4-Linear Classifier
      • Wk3-4-Decision Trees

      Question pattern: MCQ and Written.

      Date and Time: November 9, 2021 at 11:30 am.

    • Class Test-2 Quiz
      Restricted Not available unless: You belong to any group

  • Week 6: Nearest Neighbor and Bayesian Classification

    Topics of Discussion

    • Nearest Neighbor Classifier
    • Bayesian Classification


    Expected Learning Outcome

    • Understand nearest neighbor classification
    • Problem-solving using Weka

  • Week 7: Mid Exam


    Syllabus of Midterm: 
    • Week-1 to Week-6

               WK-1-Introduction
               WK-2-About Data
               Wk3-DataExploration
               Wk4-Linear Classifier
               Wk3-4-Decision Trees
               Wk5-Bayes Theorem
               Wk5-NB Classification
               Wk6-Nearest Neighbor

    Topics to be covered in the midterm exam:

    • Data Pre-processing
    • Classification and Prediction

    • CSE450 Midterm Exam (Fall 2021) Assignment
      Restricted Not available unless: You belong to any group

      Dear Students,

      Find the attached exam script template and question for your online midterm exam. Download both the answer script template and Question. Make sure you know the exam guidelines and follow them very carefully. You can upload the file in PDF(preferred), Docx, or take images of the answer scripts (If needed). Make sure you submit your answer script within 2 hours 30 minutes that means within 4:00 pm.

      In case of not being able to submit in BLC submit via this GOOGLE FORM.

      N.B: If you have any query regarding the question or face any difficulty during the exam then contact via telegram.


  • Week 8: Association Rule Mining

    Topics of Discussion

    • Association Rule Mining
    • Problem Solving using association rule mining

    Expected Learning Outcome

    • Apply the knowledge of association rule mining
    • Problem solving using Weka

    • Lab Session 7: Working with Weka or Jupyter Notebook

    • Class-1 Lecture Video (PC-B) URL
      Restricted Not available unless: You belong to PC-B Fall 2021
    • Class-1 Lecture Video (PC-H) URL
      Restricted Not available unless: You belong to PC-H Fall 2021
    • Class-1 Lecture Video (PC-G) URL
      Restricted Not available unless: You belong to PC-G Fall 2021
    • Class-2 Lecture Video (PC-B) URL
      Restricted Not available unless: You belong to PC-B Fall 2021
    • Class-2 Lecture Video (PC-G) URL
      Restricted Not available unless: You belong to PC-G Fall 2021
    • Class-2 Lecture Video (PC-H) URL
      Restricted Not available unless: You belong to PC-H Fall 2021

  • Week 9: Working with Clustering

    Topics of Discussion

    • Clustering Data
    • Problem Solving using Clustering


    Expected Learning Outcome

    • Understanding of clustering in data mining
    • Problem-solving using clustering

  • Week 10: Presentation and Assignment


    Dear Students,
    Read the file carefully and choose the topic accordingly. Go through the google sheet and insert your ID, Name, and Group Name, and the project/presentation topic of your group. Your group will consist of 4 members. The deadline to submit the information is November 30, 2021. You have to work on the selected project either using Weka or Programming Language (e.g Python). You will present your project as a presentation and submit the project report as an assignment. Two groups can not work on the same topic.
    N.B: Submit your video presentation at the following link.

    Group Submission Link: 


    Presentation Submission Link: 


    • Assignment Submission Link
      Restricted Not available unless: You belong to any group

      Submit your assignment individually within the due date.

  • Week 11: Neural Network

    Topics of Discussion

    • Neural Network
    • Application of neural network


    Expected Learning Outcome

    • Apply knowledge of neural network
    • Problem solving

  • Week 13: Final Exam


    Topics to be included in the final exam:

    • Association rule mining
    • Clustering and applications
    • Wk1-Introduction
    • Wk3-DataExploration
    For detail watch this class lecture video