授業の形態
|
|
|
| |
|
アクティブラーニング
|
|
|
| |
|
授業内容と方法
|
(* This class include Japanese students.)
This class consists of 2 or 3 parts. We learn about a general process to be ready for re-useable knowledges by data mining theories. Everyday has two or three persons have to explain/introduce about assigned references, basic theory from part 1 and applications from part 2.
According to circumstances (belong to students), we will take work time for practice about your interest application.
|
| |
|
URGCC学習教育目標
|
|
|
| |
|
達成目標
|
+ You can explain about general process of data mining.
+ You can explain/introduce about newly conference papers.
If we select part 3,
+ You can try to design/build the process to any your familiar topic.
+ You can evaluate/consider about raw data and results of your application.
|
| |
|
評価基準と評価方法
|
You must explain/introduce/discuss about assigned references.
presentation (50%), presentation documents (20%), Q&A and discussions (30%)
|
| |
|
履修条件
|
Math (especially Linear Algebra and Statistics), experiences of programming.
|
| |
|
授業計画
|
#1 (10/6): Guidance
10/13: cancelled (business trip)
Part 1: Introduction to data mining
#2 (10/20): What is Data Mining? Some definitions, simple examples and applications.
#3 (10/27): Machine Learning and Statistics, aspects of personal information.
#4 (11/3): Instances and Attributes as Input, and Knowledge Representations as Output
#5 (11/10): The basic methods and algorithms 1
#6 (11/17): The basic methods and algorithms 2
#7 (11/24): Credibile experiment designs and evaluation ways
12/1: cancelled (day of entrance exam)
Part 2: Discussion about applications
#8 (12/8): readings 1
#9 (12/15): readings 2
#10 (12/22): readings 3
Cont. Part 2 or Part 3: Practice work
#11 (1/5)： readings 4 or Consideration about applications, group making
#12 (1/12)： readings 5 or exercise 1
#13 (1/19)： readings 6 or exercise 2
#14 (1/26)： readings 7 or exercise 3
#15 (2/2)： readings 8 or Final presentation about outcomes
|
| |
|
事前学習
|
|
read carefully references.
|
| |
|
事後学習
|
|
read carefully references.
|
| |
|
教科書にかかわる情報
|
|
|
| |
|
教科書全体備考
|
|
|
| |
|
参考書にかかわる情報
|
|
|
9780123748560
|
we will read the part 1 of this book to learn the data mining theory.
|
|
Ian H. Witten, Eibe Frank, Mark A. Hall
|
|
Morgan Kaufmann
|
2011
|
|
|
|
| |
|
参考書全体備考
|
|
|
| |
|
使用言語
|
|
日本語
|
| |
|
メッセージ
|
In part 2, we will read some best papers on below conferences in recent 5 years. But I hope that you suggest us any related papers in your interest.
+ mainly application examples.
IEEE/WIC/ACM International Conference on Web Intelligence (IEEE/WIC/ACM WI)
ACM International Conference on Web Search and Data Mining (ACM WSDM)
ACM Special Interest Group on Information Retrieval (SIG-IR)
+ mainly theoretical or technical papers.
IEEE International Conference on Data Mining (ICDM)
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ACM KDD)
|
| |
|
オフィスアワー
|
|
|
| |
|
メールアドレス
|
|
この項目は教務情報システムにログイン後、表示されます。
|
| |
|
URL
|
|
http://ie.u-ryukyu.ac.jp/~tnal/2016/dm-theory/
|
| |
|
|