Topic outline

  • Welcome to Data Mining and Machine Learning

    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 Lesson 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
    Download Book Chapter:
    1. Chapter from HAN
    2. Chapter from Algorithmic ML 

  • Week 1 Lesson 2: Basic for data preprocessing And Preliminaries for Machine Learning

    Objectives: 
    • Data Objects and Attribute Types
    •  Basic Statistical Descriptions of Data
    •  Data Visualization
    • Measuring Data Similarity and Dissimilarity
    Download Book Chapter: 

    • Week 2 Lesson 1: Probability and Statistics for Machine Learning

      Topics of Discussion

      • 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



        • Midterm Examination Assignment
          Opened: Thursday, 14 July 2022, 1:30 PM
          Due: Thursday, 14 July 2022, 4:00 PM
          View

          • Submit your answer script here at the BLC course.

          • In case of any unsolvable difficulty, submit the script to the Google classroom.


      • 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

            • Assignment
              Due: Friday, 24 June 2022, 11:59 PM
              Make a submission

              Deadline: 11:59 pm on 24 June, 2022


          • Topic 15

            • Topic 16

              • Topic 17

                • Topic 18

                  • Topic 19

                    • Topic 20