授業の形態
|
|
講義
|
| |
|
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
|
*1: This class will open for below 4 classes:
old curriculum: 「データマイニング論」 and "Data Mining Theory",
new curriculum: 「データマイニング特論」 and "Advanced Data Mining",
*2: This class will include Japanese students.
*3: The class will be conducted remotely, and the Zoom URL for the first session will be provided in Webclass.
This class consists of 2 parts. We learn about a general process to be ready for re-useable knowledges by data mining theories and about machine learning models as a mining method. Everyday has about four persons have to explain/introduce about assigned references, basic theory from part 1 and applications (your research) from part 2.
|
| |
|
URGCC学習教育目標
|
|
専門性
|
| |
|
達成目標
|
+ You can explain about general process of data mining and some 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
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).
|
| |
|
評価基準と評価方法
|
You must explain/introduce/discuss about assigned references.
presentation (60%), Q&A and discussions (20%), final report (20%).
The presentation will be adjusted according to the number of participants, but it will be allocated about three or four times for Part 1, one time for Part 2.
Please note that even if you get a perfect score in the presentation and presentation documents, it will be only 80%. Voluntary participation in discussions during the weekly Q & A session is required.
|
| |
|
履修条件
|
Math (especially Linear Algebra and Statistics), experiences of programming.
|
| |
|
授業計画
|
Week 1 : Guidance
======================================
Part 1: Introduction to data mining and machine learning
Week 2
・Chap. 1, What's it all about?
・Chap. 2, Input: concepts, instances, attributes
Week 3
・Chap. 3, Output: knowledge representation
・Chap. 4, Algorithms: the basic methods, sec. 4.1 to 4.5
Week 4
・cont., Chap. 4, Algorithms, section 4.6 to remains
・Chap. 5, Credibility: evaluating what's been learned
Week 5
・Chap. 6, Trees and rules
・Chap. 7, Extending instance-based and linear models
Week 6
・Chap. 8, Data transformations, section 8.1 to 8.3
・Cont., section 8.4 to remains
Week 7
・Chap. 9, Probabilistic methods, section 9.1 to 9.5
・Cont., section 9.5 to remains
Week 8
・Chap .10, Deep learning, section 10.1 to 10.3
・Cont., section 10.4 to remains
Week 9
・Chap. 11, Beyond supervised and unsupervised learning
・Chap. 12, Ensemble learning
Week10
・Chap. 13, Moving on: applications and beyond
======================================
Part 2: Discussion about applications (your research)
Week 11 ~ 15
・two or three persons per week.
・if we have extra time, let's read conference paper.
|
| |
|
事前学習
|
|
read carefully references.
|
| |
|
事後学習
|
|
read carefully references.
|
| |
|
教科書にかかわる情報
|
|
|
978-0128042915
|
we will read the part 1 of this book to learn the data mining theory.
|
|
Ian H. Witten ... [et al.]
|
|
Morgan Kaufmann
|
2017
|
|
|
|
| |
|
教科書全体備考
|
|
|
| |
|
参考書にかかわる情報
|
|
|
9781787125933
|
we will read the part 1 of this book to learn the data mining theory.
|
|
Sebastian Raschka, Vahid Mirjalili
|
|
Packt Pub.
|
2017
|
|
|
|
| |
|
参考書全体備考
|
|
|
| |
|
使用言語
|
|
英語
|
| |
|
メッセージ
|
* In part 2, if we have extra time, 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 Friday (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/2022/adm/
|
| |
|
|