授業の形態
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講義
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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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*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.
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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 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).
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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 (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.
Q&A refers to "questions to other presenters".
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.
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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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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.
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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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978-0128042915
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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 ... [et al.]
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Morgan Kaufmann
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2017
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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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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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* 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)
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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/2023/adm/
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