Topic outline

  • Welcome to Data Mining Machine Learning Lab


    Welcome Note 


    Dear Students!

    Welcome to the world of "Data Mining and  Machine Learning Lab"  (CSE322) in Summer 2021. In this course, you are going to implement Data Mining tasks using a tool called "Weka" and you will implement Machine Learning Algorithms in a very popular coding language which is Python. Here the course is designed in a way that you will get all the support in this online platform. The course is designed with plenty of tutorials, resources, and lab work which you will get week by week. You will find here course contents, reference books, course delivery plans, all kinds of announcements, and contact information.

    So, let's start our journey and make this journey 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 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%

    • Recommended Books
    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
    7. Data Mining With Python
    • Global Data Repository for Data Mining and/or Machine Learning
    1. WISDM
    2. UCI ML Repository
    3. KDD Cup
    4. Kaggle
    5. KDnuggets

    • Standard Templates
    1. IEEE Template
    2. ACM Template

  • Week 1: Introduction to Weka

    Topics of Discussion

    • Introduction to Weka
    • Relationship to data mining
    • Overview of data mining with Weka


    Expected Learning Outcome

    • Appreciation of the needs of data mining with Weka
    • Visualization of the relationship of Weka to data mining
    • Visualization of different data mining tasks with Weka

    •              

    • Video Lecture PC-I

      Restricted Not available unless: You belong to PC-I

    • Video Lecture PC-B

      Restricted Not available unless: You belong to PC-B
    • Lab Task-1 Assignment

      Perform all the activities (Dataset load, Exploration, Building Classifier, Interpret the output) which I have shown you in class with the weather.numeric.arff dataset of Weka. Prepare a report by writing each step followed by its screenshot.  Submit a pdf file of your report here.  

  • Week 2: Data Visualization Using Weka

    Topics of Discussion

    • Review of data mining task and related application examples
    • Data Visualization with Weka
    • Course Project Team and discussion


    Expected Learning Outcome

    • On-hand acquaintance and practice of data visualization with Weka
    • Team formation for the course project

  • Week 3: Handling Missing Data and Normalization

    Topics of Discussion

    • Handling missing data with Weka
    • Normalization with Weka

    Expected Learning Outcome

    • Problem solving skills in handling missing data and normalization.
    • Skill in using Weka as a data mining tool for classification and prediction.

  • Week 4: Data Preprocessing Using Weka

    Topics of Discussion

    • Discussion on feature/attribute selection 
    • Replace data using weka
    • Project Discussion and execution plan

    Expected Learning Outcome

    • On-hand acquaintance and practice of feature/attribute selection with Weka
    • Selection of project topic by team

  • Week 5: Classification and Evaluation Using Weka

    Topics of Discussion

    • Classification and prediction with Weka
    • Classification: Decision tree, Bayesian, Instance-based

    Expected Learning Outcome

    • Problem solving skill in classification and prediction
    • Skill in using Weka as a data mining tool for classification and prediction

  • Week 6: Binning Using Filter In Weka

    Topics of Discussion

    • Discretization 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 7: Mid Exam

    Midterm Examination Week



    No Lab. There will be a post-midterm Lab Test and Assignment for the lab.


    • Week 8: Presentation of Project

      Project Presentation



      Upload your report to the following google drive within one week after your presentation. Each team will upload one report in the format given in Project Guideline and upload your report in pdf. Rename the file by your Group name and Title before uploading. 

      • Week 9: Introduction to Python and Numpy

        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
        • Visualization of the relationship of Python to machine learning
        • Visualization of different machine learning tasks with Python

      • Week 10: Introduction to Python and Pandas

        Topics of Discussion

        • Introduction to Pandas
        • Relationship to machine learning
        • Overview of machine learning with Pandas


        Expected Learning Outcome

        • Appreciation of the needs of machine learning with Pandas
        • Visualization of the relationship of Pandas to machine learning
        • Visualization of different machine learning tasks using Pandas.

      • Week 11: 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 12: Lab Performance Test

        Dear Students,
        A Lab Test will be held online on the BLC platform on August 21 at 7:30 pm. 

        Syllabus:

        • Python Basics 
        • Numpy
        • Pandas
        • Classification Using Python

        For details check google classroom.

        • LAB PERFORMANCE TEST

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        • Lab Test Part-1 (MCQ) Quiz
          Restricted Not available unless: You belong to any group
        • Lab test Part-2 (Code and written) Assignment
          Restricted Not available unless: You belong to any group