Spring 2019, M1522.000900 Introduction to Data Mining, Seoul National University

Seoul National University
M1522.000900 Data Mining
Spring 2019 - U Kang

News and Announcements

The first class will be held on March 4.
We do not accept requests for additional registration form (Choangi).

Course Information

Data mining refers to theories and techniques for finding useful patterns from massive amount of data. Data mining has been used in high impact applications including web analysis, fraud detection, recommendation system, cyber security, etc. This course covers important algorithms and theories for data mining. Main topics include mapreduce, finding similar items, mining frequent patterns, link analysis, data stream mining, clustering, graphs, and mining big data.

Instructor: U Kang (ukang at snu.ac.kr)
- 302-106
- Office hours: Monday 14:30 ~ 15:20 (also, by appointment)
TA
- Bonhun Koo (darkgs@snu.ac.kr), 301-519, office hours: TBD
- Chun Quan (quanchun@snu.ac.kr), 301-519, office hours: TBD
- Shuyu Wang (wangshuyu79@gmail.com), 301-519, office hours: TBD
Class meets:
- Mon, Wed 15:30 ~ 16:45 (Room 302-106)
URL of this web page: http://datalab.snu.ac.kr/~ukang/courses/19S-DM

Schedule

Date		Topic	Notice
Mar	4	Introduction
	6	Basics (Chapter 1)
	11	Map-Reduce and the New Software Stack (Chapter 2)
	13	Map-Reduce and the New Software Stack 2 (Chapter 2)	HW1 out (due: Mar. 27)
	18	Finding Similar Items (MMDS Chapter 3)
	20	Finding Similar Items (MMDS Chapter 3): use the previous lecture slide
	25	Finding Similar Items-3 (Chapter 3): use the previous slide	HW2 out (due: Apr. 1)
	27	Mining Data Streams (Chapter 4)
Apr.	1	Mining Data Streams-2 (Chapter 4)
	3	Mining Data Streams-3 (Chapter 4)	HW 3 out (due: Apr. 10)
	8	Link Analysis (Chapter 5)
	10	Link Analysis-2 (Chapter 5)
	15, 17	Midterm
	22	Link Analysis-3 (Chapter 5)
	24	Frequent Itemsets (MMDS Chapter 6)
	29	Frequent Itemsets-2 (MMDS Chapter 6)
May	1	Clustering (MMDS Chapter 7)
	6	Clustering (MMDS Chapter 7): use the previous slide
	8	Clustering-2 (MMDS Chapter 7)
	13	Advertising on the Web (MMDS Chapter 8)
	15	Advertising on the Web-2 (MMDS Chapter 8)
	20	Recommendation (MMDS Chapter 9)
	22	Recommendation-2 (MMDS Chapter 9)
	27	Mining Social-Network Graphs (MMDS Chapter 10)
	29	Mining Social-Network Graphs-2 (MMDS Chapter 10)
June	3	Dimensionality Reduction (MMDS Chapter 11)
	5	Dimensionality Reduction-2 (MMDS Chapter 11)
	10	Conclusion
	12	Final

Grading

10% Attendance and Quiz
25% Homework
30% Midterm
35% Final
(+5% Participation)

Late policy - for all deliverables:

No delay penalties, for medical etc emergencies (bring doctor's note).
Each person has 4 'slip days' total, for the whole semester. 10% per day of delay, after that.

Textbook

The text book is
Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman. (available in online)

Prerequisite

We expect you to have an undergraduate-level knowledge on the following topics:

Algorithms
Basic probability
Programming
Linear Algebra

We provide some background, but the class will be fast paced.

Last modified Feb. 12, 2019, by U Kang

Seoul National University M1522.000900 Data Mining Spring 2019 - U Kang