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Topic outline
- Welcome to Data Mining and Machine Learning
Welcome to Data Mining and Machine Learning
Welcome Note
Hello Students!
Welcome to the world of "Data Mining and Machine Learning" (CSE321) in Fall 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
-3, Daffodil Smart City, Ashulia Campus, Dhaka
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%
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- Introduction to Data Mining by Tan
- Machine Learning by Tom Mitchell
- Reference Reading Materials
- Introduction to Data Mining and Applications
- Data Mining Concepts and Techniques
- Data Mining Techniques
- IEEE Template
- ACM Template
Class Schedule:
- PC-B: Sunday 4:00-5:30 pm, Wednesday 2:30-4.00 pm
Telegram Link:
Google Meet Link:
- PC-B: https://meet.google.com/iiq-kgmi-fnk
Quick Access
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Quiz-2
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Presentation
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- Week 1: Introduction
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
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DISCUSSION FORUM
- Week 2: Differences between Data Mining and Machine Learning
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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Lecture Video Class-1
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DISCUSSION FORUM
- Week 3: Data
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
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DISCUSSION FORUM
- Quiz
Quiz
Quiz-1 Syllabus:
- Topic-1: Introduction
- Topic-2: Difference between Data Mining and Machine Learning
- Topic-3: Data
Question Pattern: MCQ, Written.
- Week 4: Exploring 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
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DISCUSSION FORUM
- Week 5: Introduction to Classification
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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ASSIGNMENT
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
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
CLASS LECTURE VIDEO
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- Week 7: Mid Examination
Week 7: Mid Examination
Syllabus of Midterm:
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
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: Classification and Prediction: Alternative Techniques
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
CLASS LECTURE VIDEO
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DISCUSSION FORUM
#Presentation
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. Two groups can not work on the same topic.
N.B: The presentation date will be announced later.
- Week 9: Cluster Analysis
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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DISCUSSION FORUM
- Week 10: Association Rule Mining
Week 10: Association Rule Mining
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 11: Cluster Analysis (Continued)
Week 11: Cluster Analysis (Continued)
- Quiz
- Week 14: Final Examination
Week 14: Final Examination
Syllabus of Final exam:
- Cluster Analysis ->up to slide #74
- Association Rule Mining ->up to slide # 36
For details watch this video.
*****The exam will be held physically.