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Syllabus

Infomation of Course

Program Master/Doctorate [석/박사과정] Course Type Elective(Graduate) [ 선택(석/박사) ]
Course Code 31.646 Course No IE646
Section English English
L:L:C(AU) 3:1:3.0(0) Exam time
(classroom)
- Mon: 09:00~11:45
()
Course Title Data Mining [ 데이터 마이닝 ]
Class time
(classroom)
Mon: 09:00~10:30 / (E11)Creative Learning Bldg. [ (E11)창의학습관 ] (103)
Wed: 09:00~10:30 / (E11)Creative Learning Bldg. [ (E11)창의학습관 ] (103)
Notice 학.석사 상호인정, Code Share KSE525

Information of Professor

Name 이재길(Lee, Jae-Gil)
Department 산업및시스템공학과(Department of Industrial & Systems Engineering)
Phone 042-350-1617
E-Mail jaegil@kaist.ac.kr

Plan of Lecture

Syllabus File Syllabus (KSE525).pdf
Syllabus URL
Summary of Lecture Data mining plays an important role in discovering useful knowledge from huge amounts of data. This course teaches the basic concepts and methods of data mining. More specifically, frequent patterns and associations; classification and prediction; and cluster analysis will be covered. The main goal of this course is to give the students a broad knowledge of various data mining methods without confining to a specific domain. This course is intended as a prerequisite for advanced data mining courses and thus is suitable for both undergraduate and graduate students. The students will understand how data mining can be exploited for discovering useful knowledge.
Material for Teaching Main textbook: Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011.
Auxiliary textbook: Yanchang Zhao, R and Data Mining: Examples and Case Studies, Academic Press, 2013.
Auxiliary textbook: Norman S. Matloff, The Art of R Programming: A Tour of Statistical Software Design, No Starch Press, 2011.
Auxiliary textbook: John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy, Fundamentals of Machine Learning for Predictive Data Analytics, MIT Press, 2015.
Evaluation Criteria - Midterm exam: 30%
- Final exam: 40%
- Assignments: 20% (latency penalty: 20%)
- Project: 10%
Lecture Schedule - 1st Week: Introduction
- 2nd Week: Getting to Know Your Data
- 3rd Week: Data Preprocessing
- 4th Week: Frequent Patterns and Associations
- 5th Week: Frequent Patterns and Associations
- 6th Week: Classification and Prediction
- 7th Week: Basics of R Programming
- 8th Week: Midterm Exam
- 9th Week: Classification and Prediction
- 10th Week: Classification and Prediction
- 11th Week: Cluster Analysis
- 12th Week: Cluster Analysis
- 13th Week: Cluster Analysis, Applications and Trends in Data Mining
- 14th Week: Case Study I
- 15th Week: Case Study II
- 16th Week: Final Exam
Memo Assistant Name: TBD
Prerequisite: Basic programming skills, Background knowledge on algorithms and data structures