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The 4th Knowledge Service Workshop : Techniques in Prediction Analysis for the Inudstry

The Department of Knowledge Service Engineering, KAIST, invites you to the 4th Knowledge Service Workshop!

We are thankful for your support throughout the years. This year’s workshop will be about “Techniques in Prediction Analysis for the industry”.

We hope for your interest and participation, the details of the event are available both on this page and the department website.


The 4th Knowledge Service Workshop : Techniques in Prediction Analysis for the Inudstry


■ Date : February 25, 2016(Thursday), 10 :00 ~ 16:00

■ Location : COEX  Room 318

■ Hosted By : KAIST Department of Knowledge Service Engineering

■ Sponsored by : Bit computer

■ Supported by : ZDnet Korea, Korean Institute of Information Scientists and Engineers, Korea Big Data Forum, The society of Service Science, HCI Korea

■ Webpage  : http://kseworkshop.kaist.ac.kr/2016


■ Program

  10:00~10:10 Opening

  10:10~10:50 Data Science Overview and Introduction, Analysis and Data Science (Prof. Wanchul Yoon, KAIST)

  10:50~11:30 Advertisement and Promotion & preference and selection (Prof. Mun Yi, KAIST)

  11:30~13:00 Lunch(Provided)

  13:00~13:40 Grocery bag analysis & Spatial data Analysis (Prof. Jaegil Lee, KAIST)

  13:40~14:20 Economic Data Analysis & Management(Prof. Youngjae Jang, KAIST)

  14:20~14:40 Coffee Break(Provided)

  14:40~15:20 Text mining & Sentiment Analysis(Prof. Gahgene Gweon, KAIST)

  15:20~16:00 Sports Analysis & Brands and Prices(Prof. Uichin Lee, KAIST)


■ Registration: Normal  \200,000 , Student \150,000

■ Early-bird Registration : At the website (http://kseworkshop.kaist.ac.kr/2016), 

Deadline: February 23rd(Tuesday) 6pm or First 120

The registration fee will be fully refunded if cancelled before February 16th(Tuesday), afterwards no refund will be available.

Please be noted that we do NOT accept on-site registration

■ Other

   - We will be providing the following textbook for those who attend the workshop 
(Techniques for Prediction Analysis Modelling: Case studies of Big data analysis using R – Thomas Miller, Translated into KOREAN by Sabeum Jung, published by Acorn Publisher )


■ Contact : Youngha Choi, Department of Knowledge Service Engineering KAIST (T.042-350-1603 / E-mail: yhchoi@kaist.ac.kr)











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