授業の形態
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アクティブラーニング
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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
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授業内容と方法
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(* 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.
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URGCC学習教育目標
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専門性
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達成目標
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+ You can explain about general process of data mining or a few models in machine learnings. [Part 1] (Expertise) 3. Applied Subjects
+ You can explain/introduce about newly conference papers. [Part 2] (Expertise) 3. Applied Subjects
+ You can point out and discuss matters of concern from a fair perspective while being interested in each model and case and thinking independently. [Part 1, 2] (Expertise, Creativity, Ethics) 5. Related Subjects
If we select part 3,
+ You can try to design/build the process to any your familiar topic. (Expertise)
+ You can evaluate/consider about raw data and results of your application. (Expertise)
Note:
+ The numbers in parentheses at the end of each item indicate the correspondence with URGCC-advanced (http://www.ged.skr.u-ryukyu.ac.jp/divisions/division-2/kaihatusitu).
+ The numbers also show the correspondence with the curriculum policy (https://www.u-ryukyu.ac.jp/admissions/3policy/gra_curriculumpolicy/#cat13).
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評価基準と評価方法
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You must explain/introduce/discuss about assigned references.
presentation (50%), presentation documents (20%), Q&A and discussions (30%)
The presentation will be adjusted according to the number of participants, but it will be allocated once or twice in each part.
Please note that even if you get a perfect score in the presentation and presentation documents, it will be only 70%. Voluntary participation in discussions during the weekly Q & A session is required.
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履修条件
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Math (especially Linear Algebra and Statistics), experiences of programming.
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授業計画
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#1 : Guidance
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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)
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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
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事前学習
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read carefully references.
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事後学習
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read carefully references.
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教科書にかかわる情報
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教科書全体備考
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参考書にかかわる情報
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9780123748560
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we will read the part 1 of this book to learn the data mining theory.
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Ian H. Witten, Eibe Frank, Mark A. Hall
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Morgan Kaufmann
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2011
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9781787125933
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we will read the part 1 of this book to learn the data mining theory.
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Sebastian Raschka, Vahid Mirjalili
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Packt Pub.
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2017
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参考書全体備考
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使用言語
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英語
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メッセージ
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* required: G-mail
* 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)
*** IMPORTANT ***
(1) message on 4th April.
https://translate.google.com/translate?hl=en&sl=auto&tl=en&u=https%3A%2F%2Fwww.tec.u-ryukyu.ac.jp%2Fblog%2F2020%2F04%2F03%2Fコロナウイルス感染症防止について%2F
For Coronavirus Infection Prevention, all classes with "face-to-face" under Faculty of Engineering will begin on April 23 (Thu). As an alternative, (1) please read a textbook from chapter 1 to chapter 2, and run examples on your PC. (2) If you have any questions, please contact me with email.
P.S.
For download the textbook, you should tell me G-mail address. After that, I will athorize you to access it.
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オフィスアワー
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room: 705, eng. bldg. #1
office hour: 12:50-14:20 on Friday (in progress)
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メールアドレス
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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。
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URL
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http://ie.u-ryukyu.ac.jp/~tnal/2020/dm-theory/
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