タイトル

科目番号 教室 登録人数 履修登録方法 対面/遠隔
        10   抽選対象   対面授業  
開講年度 期間 曜日時限 開講学部等
2018 前学期 火5 理工学研究科情報工学専攻  
講義コード 科目名[英文名] 単位数
R01680001 Data Mining Theory   2  
担当教員[ローマ字表記]
當間 愛晃  
授業の形態
 
 
アクティブラーニング
学生が議論する、学生が自身の考えを発表する、学生が文献や資料を調べる
 
授業内容と方法
(* This class will 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, or about machine learning models as a mining method. 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 consideration time as part 3 for practice about your-own work or interest applications.
 
URGCC学習教育目標
専門性
 
達成目標
+ You can explain about general process of data mining or a few models in machine learnings.
+ 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 : Guidance

======================================
Part 1: Introduction to data mining or machine learning

#2 : What is Data Mining? Some definitions, simple examples and applications.
#3 : Machine Learning and Statistics, aspects of personal information.
#4 : Instances and Attributes as Input, and Knowledge Representations as Output
#5 : The basic methods and algorithms 1
#6 : The basic methods and algorithms 2
#7 : Credibile experiment designs and evaluation ways


#2 : What is Machine Learning?
#3 : Classification by perceptron learning
#4 : Classification models in Scikit-learn
#5 : Building good training sets and pre-processing
#6 : Compressing data via dimensionality reduction
#7 : Model evaluation and Hyperparameter tuning
#8 : reserve day (e.g., adjustments for Part 2)

======================================
Part 2: Discussion about applications
#9 : readings 1
#10 : readings 2
#11 : readings 3
#12 : readings 4
#13 : readings 5
#14 : readings 6
#15 : readings 7
 
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
参考書 書名 ISBN
9780123748560
備考
we will read the part 1 of this book to learn the data mining theory.
著者名
Ian H. Witten, Eibe Frank, Mark A. Hall
出版社
Morgan Kaufmann
出版年
2011
NCID
参考書 書名 ISBN
1783555130
備考
we will read the part 1 of this book to learn the basis of machine learning.
著者名
Sebastian Raschka
出版社
Packt Publishing
出版年
2015
NCID
 
参考書全体備考
 
 
使用言語
英語
 
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
room: 705, eng. bldg. #1
office hour: 12:50-14:20 on Thursday (in progress)
 
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/2018/dm-theory/
 

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