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CSE 450 Course Plan Notes Summer 2016 Se

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CSE 450 Course Plan Notes – Summer 2016

Section: F Course Instructor: Mohammad Mahmudur Rahman Grading Plan:

Theory

Final exam 40

Mid-term exam 25

3 Class tests [Spot test] 15

1. Data Preparation and cleaning 2. Classification and Prediction 3. Clustering

Attendance 7

Assignment: 2 (1 on dataset preparation + 1 on data mining) 5

Presentation: 2 (Based on assignment) 8

Total 100

Bonus marks

A) Marks awarded in class 5

B) Performance of a Kaggle contest 5

Activity List

Assignment 1 opens Theory 3rd week (May 21 – May 27) Classroom

Class Test 1 Theory 4th week (May 28 - June 3) Classroom

Assignment 1 submission and presentation

Theory 7th week (June 18 - June 24) Classroom

Mid-term exam Theory 8th week (June 25 - July 1) Exam hall

Assignment 2 opens Theory 9th week (July 9 – July 15) Classroom

Class Test 2 Theory 10th week (July 16 – July 22) Classroom

Class Test 3 Theory 13th week (August 6 – August 12) Classroom Assignment 2 submission and

presentation

Theory 14th week (August 13 – August 19) Classroom

Final exam Theory 16th

week (August 27 – September 3)

Exam hall

Course Reference Material

Name Author ISBN-13

Data Mining: Concepts and Techniques, Third Edition

Jiawei Han, Micheline Kamber and Jian Pei 978-0123814791

orange.biolab.si/getting-started/

www.kaggle.com

Curriculum Design

Theory 1.

• Introduction

• Course logistics

• Overview of data mining tasks

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2.

• Data Warehousing

• Introduction to Orange and Weka 3.

• Data mining processes

• Data Cleaning and Dataset preparation

• Dividing dataset into training, validation and test sets

• Finding ready made dataset • Assignment 1 discussion 4. Association Rule Mining

5. Neural Network 6.

• Classification and Prediction • Regression

• Tree based approaches

• Using Neural Network

7.

• Clustering

• Assignment 2 discussion 8. CRISP-DM

9.

• Time Series Mining • Mining Data Streams 10.

• Multi-Relational Data Mining • Data Mining for Fraud Detection

11.

• Introduction to Recommender System

• Collaborative filtering

12.

• Data Mining Applications in real life

• Research problems

Referensi

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