## 計算論的学習理論

JA | EN

 科目ナンバリング G-ENG76 63173 LE10 開講年度・開講期 2020・後期 単位数 2 単位 授業形態 講義 配当学年 博士 対象学生 大学院生 使用言語 英語 曜時限 水1 教員 山本　章博（情報学研究科 教授） 小林　靖明（情報学研究科 助教） 授業の概要・目的 Machine learning now makes string impact to our daily life. In this course we treat machine learning from discrete data and present its mathematical foundations based on formal language theory and theory of computation. Machine learning techniques based on neural networks are suited for real valued vector data, but are not always for discrete structured data. In this course we provide learning mechanism without neural networks. First we introduce elements needed in formalizing machine learning, and then we explain learnability of various classes of formal languages in the models of identification in the limit and learning with queries. We also introduce some results presented recently in computational learning theory, including its relationship with first-order logic as well as with ideals of polynomials. Secondly, we introduce frequent itemset mining from fixed length of bit-vectors. We also give some extensions including mining closed itemsets, mining frequent substrings as well as subtrees. 到達目標 By taking this course, students are expected to understand mathematical foundations of machine learning from string data, tree data, and bit-vectors of a fixed length. 授業計画と内容 1. Introduction: Machine learning from discrete data 2. Learning pattern languages from String Data3. Correctness of learning4. Learning regular languages without queries5. Learning regular languages with queries6. Learning unions of pattern languages7. Elementary formal systems and learning8. Learning tree pattern languages9. Learning polynomial ideals in algebra10. Frequent itemset mining11. Formal concept analysis and learning12. Frequent substring mining13. Frequent subtree mining14. Recent results on learning from discrete data (1)15. Recent results on learning from discrete data (2) 成績評価の方法・観点 Evaluation is based on the submitted reports on the assignments, which will be provided twice during the course. 履修要件 Students are assumed to have fundamental knowledge on mathematics, in particular, set theory, and also to be familiar to algorithms. 授業外学習（予習・復習）等 Every week, students should review the slides and documents for the lecture which will be available on the lecturer's homepage http://www.iip.ist.i.kyoto-u.ac.jp/member/akihiro/lectures/lectures.htmland also in KULASIS. 参考書等 Grammatical Inference: Learning Automata and Grammars, Colin de la Higuera, (Cambridge University Press), ISBN: ISBN:0521763169 計算論的学習, 榊原康文, 横森貴, 小林聡 , (培風館), ISBN: ISBN:4563014966