Topic outline

  • Welcome to Data Mining and Machine Learning


     Welcome Note 


    Hello Students!

    Welcome to the world of "Data Mining and  Machine Learning" (CSE321) in Summer 2021. In this course, you are going to learn the fundamental concepts of Data Mining and  Machine Learning. You will get to know some basic tasks and algorithms which are related to data mining and machine learning 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 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
    • Why data mining is a discipline?
    • Overview of data mining tasks: Classifications, Regression, Clustering.


    Expected Learning Outcome

    • Appreciation of the needs of data mining
    • Visualization of data warehouse 
    • Visualization of different data mining tasks


    Learning Resource

    • CLASS LECTURE VIDEO

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    • Video Class-1 of PC-B

      Restricted Not available unless: You belong to PC-B

    • Video Class-1 of PC-I

      Restricted Not available unless: You belong to PC-I
    • Video Class-2 of PC-B
      Restricted Not available unless: You belong to PC-B
    • Video Class-2 of PC-I
      Restricted Not available unless: You belong to PC-I

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      DISCUSSION FORUM

  • Week 2: Differences between Data Mining and Machine Learning

    Topics of Discussion

    • Overview of data mining tasks: Clustering, Association Rule Mining, etc.
    • Review of data mining tasks and related application examples.
    • Overview of Machine learning, the difference between Data Mining and Machine Learning.


    Expected Learning Outcome

    • Visualization of data warehouse and relationship to data mining
    • Visualization of different data mining tasks
    • Analyze the difference between Data Mining and Machine Learning.


    Learning Resource

    • CLASS LECTURE VIDEO

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    • Video Class-1 of PC-B
      Restricted Not available unless: You belong to PC-B

    • Video Class-1 of PC-I

      Restricted Not available unless: You belong to PC-I
    • Video Class-2 of PC-B
      Restricted Not available unless: You belong to PC-B

    • Video Class-2 of PC-I

      Restricted Not available unless: You belong to PC-I

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      DISCUSSION FORUM

  • Week 3: Data

    Topics of Discussion

    • Overview of Data, Types of Data, Data Quality.
    • Introduction to Data Preprocessing; Measures of Similarity and Dissimilarity.

    Expected Learning Outcome

    • Gather knowledge on Data, Types of Data, Data Quality.
    • Analyze Data Preprocessing; Measures of Similarity and Dissimilarity.

    Learning Resources

    • Lecture Slide [Data]

  • Week 4: Exploring Data

    Topics of Discussion

    • Discussion on data mining process: Data preparation and cleansing and task identification
    • Introduction to OLAP

    Expected Learning Outcome

    • Gather knowledge on Data preparation and cleansing and task identification.
    • Analyze Data Visualization.

    Learning Resources

  • Quiz

    Quiz-1  Syllabus:

    • Topic-1: Introduction
    • Topic-2: Difference between Data Mining and Machine Learning
    • Topic-3: Data

    For details check google classroom.

    • Quiz-1 (PC-I)
      Restricted Not available unless: You belong to PC-I
    • Quiz-1 (PC-B)
      Restricted Not available unless: You belong to PC-B

  • Week 5: Introduction to Classification

    Topics of Discussion

    • Classification and Prediction
    • Classification: tree-based approaches

    Expected Learning Outcome

    • Gather knowledge on tree-based classification approaches.
    • Analyze and implement tree-based classification algorithms.

    Learning Resources

    • CLASS LECTURE VIDEO

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    • Video Lecture on Topic-5.1 [Linear Classifier] [PC-B]

      Restricted Not available unless: You belong to PC-B

    • Video Lecture on Topic 5.1 [Linear Classifier] [PC-I]

      Restricted Not available unless: You belong to PC-I

    • Video Lecture on Topic 5.2 [Decision Tree] [PC-B]

      Restricted Not available unless: You belong to PC-B

    • Video Lecture on Topic 5.2 [Decision Tree] [PC-I]

      Restricted Not available unless: You belong to PC-I

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      ASSIGNMENT
    • Assignment-1
      Restricted Not available unless: You belong to any group

      In the attached file there are two questions. Write the solution in your script (Handwritten), capture image, add them to a word file and convert it to pdf. If you have no option to use a computer then just attached the captured image.  Whichever method you use, submit the file here as attachments within the due date. Do not forget to write your name and ID at the upper corner of your word file and also save the file with your Student ID.

  • Week 6: Classification and Prediction: Continuation of Decision Tree

    Topics of Discussion

    • Classification and Prediction
    • Classification: tree-based approaches

    Expected Learning Outcome

    • Gather knowledge on tree-based classification approaches.
    • Analyze and implement tree-based classification algorithms.

    Learning Resources

  • Week 7: Mid Examination



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

               Topic - 1:  Introduction
               Topic - 2:  Difference between Data Mining and Machine Learning
               Topic - 3: Data
               Topic - 4: Exploring Data
               Topic - 5.1: Linear Classifier
               Topic - 5.2: Decision Trees


  • Week 8: Classification and Prediction: Alternative Techniques

    Topics of Discussion

    • Nearest-neighbor classifiers
    • Bayesian classifiers

    Expected Learning Outcome

    • Gather knowledge on Nearest-neighbor, Bayesian classifiers classification approaches.
    • Analyze and implement these classification algorithms.

    Learning Resources

  • Week 9: Cluster Analysis

    Topics of Discussion

    • Clustering Data
    • Problem Solving using Clustering

    Expected Learning Outcome

    • Gather knowledge on Clustering, different clustering approaches.
    • Analyze and implement clustering algorithms.

    Learning Resources

    • CLASS LECTURE VIDEO

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    • Video Lecture of Class-1 (PC-I)

      Restricted Not available unless: You belong to PC-I

    • Video Lecture of Class-1 (PC-B)

      Restricted Not available unless: You belong to PC-B

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      DISCUSSION FORUM
    • Discussion on Week-9 Forum
      Restricted Not available unless: You belong to any group

  • Week 10: Cluster Analysis (Continued) and Association Rule Mining


    Topics of Discussion

    • Clustering Data
    • Association Rule Mining
    • Problem Solving using association rule mining

    Expected Learning Outcome

    • Gather knowledge on Clustering, different clustering approaches.
    • Analyze and implement clustering algorithms.

    Learning Resources


  • Quiz

    Quiz-2  Syllabus:

    • Nearest-Neighbor Classifier and Bayesian Classifier   
    • Cluster Analysis
    • Association Rule Mining (Upto 19 slides)

    For details check google classroom.

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

      Dear Students,
      When you finish, click the submit button. Make sure you click the submit button before the time expires (60 minutes). Otherwise, your answer will not be counted. Also, read the questions carefully.

  • Week 12: Association Rule Mining (Continued)


    Topics of Discussion

    • Association Rule Mining
    • Problem Solving using association rule mining

    Expected Learning Outcome

    • Gather knowledge on Association Rule Mining approaches.
    • Analyze and implement Association Rule Mining algorithms.

    Learning Resources


  • Week 14: Final Examination


    Syllabus of Final exam:

    • Nearest-Neighbor Classifier and Bayesian Classifier
    • Cluster Analysis 
    • Association Rule Mining ->up to slide # 41 


    For details check google classroom.

    • Final Examination CSE321 Assignment
      Restricted Not available unless: You belong to any group

      Dear Students,

      Find the attached exam script template and question for your online final 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 taking images of the answer scripts (If needed). Make sure you submit your answer script within 3 hours 30 minutes that means within 5:00 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 join the google meet with this link: meet.google.com/kpf-gbqk-ave