短期、長期の天気予報や地球温暖化の予測に関わる基礎的な事項を学び、問題点や課題を議論する。/ Students will learn about the fundamentals involved in short- and long-term weather forecasting and global warming prediction, and discuss problems and issues.

Statistics and biometrics have emerged as crucial disciplines not only in agricultural sciences but also in various other fields. This significance is primarily attributed to three factors: Firstly, advancements in data measurement techniques have facilitated the collection of extensive and diverse biological and agronomic data that were previously unattainable. Secondly, the evolution of data science methodologies has enabled the integration and modeling of such collected data. Thirdly, the enhancement of computational capabilities has empowered the utilization of these methodologies. These advancements have rendered statistical and biometric methods indispensable for extracting insights from the vast and varied biological and agronomic datasets. Throughout this lecture series, a diverse array of biological and agronomic datasets will serve as illustrative examples to demonstrate various analytical methods. Delivered in a hands-on format, utilizing R, Python, and Matlab, the aim is to equip students with practical analysis skills. The initial portion of the course, spanning the first one-third, will focus primarily on techniques for summarizing, visualizing, and modeling relationships within multivariate datasets. In the subsequent one-third, students will delve into linear models, linear mixed models, local regression, and nonlinear models. Finally, in the last segment, students will explore image analysis, machine learning, and deep learning methods. While the course will cover a broad spectrum of methods, ranging from introductory to advanced levels, the emphasis will be on developing the capability to independently conduct analyses rather than on elaborating on the theoretical underpinnings of the methods.

授業の目標、概要：自然科学では様々な現象のモデル化を行い、観測結果とモデルに基づいた計算結果を比べることで、モデルの成否を判断し、良いモデルは科学理論として受け入れられる。本講義では、物質の性質、すなわち物性科学にテーマを絞り、自然現象がモデルによっていかに説明されるか学んでもらう。 例えば、リニアモーターカー、スーパーコンピューター、量子通信技術といった最先端の科学技術は，物質の性質，例えば電気伝導度，比熱，屈折率，磁性などを巧みに制御することによって機能している。これらの性質は、物質中のの電子の応答の結果である。 しかし、物質中の電子は、莫大な数があり、直感の通じない量子の世界にあるため、物質の性質と電子応答を関係づけるのは困難である。このため、電子の応答を上手にモデル化し、また多数の粒子に特有な現象をモデル化することで、物質の性質と電子の応答の関係を解説する。 本講義は、統合自然科学科・物質基礎科学コースの物理・化学の教員により行われ、実験を専門とする教員は演示実験を取り入れ、理論を専門とする教員はエレガントな学問体系や理論モデル、コンピュータを利用した先端的な科学について分かりやすく解説する。

Please check the grading scale that applies to this course. If the Course Code ends without “- P/F”, this is a course with a letter grade (A+, A, B, C, F). If the Course Code ends with “-P/F”, this is a Pass/Fail Course The later Wittgenstein’s views about language and meaning, especially as presented in Philosophical Investigations, are among the most important arguments in twentieth century philosophy, exerting great influence not only over traditional philosophy, but also a diverse and widespread range of fields in the humanities, social sciences, and cognitive science. This course will focus on Wittgenstein’s transition from the theory of language as a logical picture of reality outlined in the Tractatus, still in essence a dominant view of language, to his later understanding of language and meaning in terms of language’s use in social “language games”. The three most significant arguments for the development of this general view in Philosophical Investigations will be studied: meaning as use; considerations about rule following and the consequences for intentionality and representation; the impossibility of a private language. A general understanding of the later Wittgenstein’s anti-theoretical philosophical method will emerge along with an understanding of the relevance of the later Wittgenstein for current philosophical questions about language.

Please check the grading scale that applies to this course. If the Course Code ends without “-P/F”, this is a course with a letter grade (A+, A, B, C, F). If the Course Code ends with “-P/F”, this is a Pass/Fail Course The later Wittgenstein’s views about language and meaning, especially as presented in Philosophical Investigations, are among the most important arguments in twentieth century philosophy, exerting great influence not only over traditional philosophy, but also a diverse and widespread range of fields in the humanities, social sciences, and cognitive science. This course will focus on Wittgenstein’s transition from the theory of language as a logical picture of reality outlined in the Tractatus, still in essence a dominant view of language, to his later understanding of language and meaning in terms of language’s use in social “language games”. The three most significant arguments for the development of this general view in Philosophical Investigations will be studied: meaning as use; considerations about rule following and the consequences for intentionality and representation; the impossibility of a private language. A general understanding of the later Wittgenstein’s anti-theoretical philosophical method will emerge along with an understanding of the relevance of the later Wittgenstein for current philosophical questions about language.

　この授業は、「個人や社会をどのようにモデル化するか」という問題を検討することを通じ、個人・社会・法の関係を検討するための視点を養うことを目的としています。 　法が社会の中でどのように作用するかを説明・予測する場合、少なくとも次の点に答える必要があります。 　（１）法に対して、人々（個人や企業）はどのように反応するのか。 　（２）上記の意思決定や行動の結果として、社会はどのようになるのか。 　人々の行動や意思決定がどういうものであるか、それらの行動や意思決定が相互にどのように影響を及ぼしあっているか、あるいは、個人の行動がどのような帰結をもたらすか、といったことを考えるために有用なのが「モデル」です。 　モデルは何も学術的な世界だけにあるのではなく、どんな法律も、個人や社会に関するモデルを何らかの形で想定していると言えます。そして、そのモデルは法分野の間で異なっている場合もあります（たとえば民法、刑法、労働法、消費者法などでどのような人間像が前提とされ、法の対象である社会をどういうものと想定しているかを考えてみてください）。そこで考えられているモデルが妥当なのか、より良いモデルとはいかなるものなのかを検討することは、基礎法学だけでなく実定法学にとっても決して無意味ではありません。 　さらに言えば、法の効果をデータによって実証的に示したいときにも適切なモデルを選択して分析を行う必要があるため、上記の問題について立ち止まって考えてみることは役に立つかもしれません。 　本授業では、個人や社会の関係を描写したり社会現象を予測したりするためのモデル（数理モデルに限定されません）を取り上げながら、「社会における法の機能」について参加者の皆さんと一緒に研究・議論したいと考えています。 　詳細は参加者の関心に応じて調整したいと思います。ただし、少なくとも下記のテーマは扱う予定です。 ・モデルと社会科学 ・ルールと人間行動 ・個人の行動に関するモデル（合理的選択モデル、行動経済学のモデル、ルールベースモデルなど） ・ゲーム理論のモデル（協力、集団行動、シグナリングなど） 　法学政治学研究科および法学部の授業ですが、他の研究科・学部の方のご参加も歓迎します。

At the end of the course, the student should have familiarised with the key concepts informing epistemological and methodological debates in social science, such as hypothesis, theory, falsification, verification, covering law, causality, with particular consideration for positivistic paradigms and anti- or post-positivistic reactions. The student should be aware of the specific difficulties of social scientific disciplines in studying human societies arising from the problematic separation between the studied object and the studying subject, and the link between social science and the political dimension. He should familiarise with problems of social research design, starting from the specificities characterizing the various stages of social science research. The student should become aware of the functional classification of social science methods in qualitative and quantitative, familiarising with a number of techniques in both domains, while grasping that contemporary social research increasingly requires the application of different methods and interdisciplinary approaches, which also lead to the necessity of teamwork coordination. The course also aims at providing a large variety of examples where the concepts and principles described above find their application and didactic clarification. Those examples will focus in particular on the study of technology and its social impact. Finally, the student should become aware of the moral implications, limits and rules informing social science research, as well as of the role and importance of effective communication in the social sciences.

This course confronts philosophy's most profound questions about the human condition with data-driven insights from modern behavioral science. For millennia, philosophers have used reason and introspection to ask: What is knowledge? What does it mean to be moral? What is love and happiness? How to deal with human violence? How to be free? What is the self? In this class, we will place these classical debates about the human nature/condition in direct conversation with cognitive psychology, behavioral economics, and social/evolutionary psychology. We will explore how far science can answer philosophical questions. <<Learning Goals>> - Identify and articulate key classical philosophical problems in epistemology (knowledge), ethics (morality), and metaphysics (self, love, free will). - Explain core concepts from behavioral science, such as dual-process theory, cognitive biases, moral foundations theory, and affective forecasting, while reasoning about major philosophical inquiries. - Analyze how empirical findings from behavioral science challenge, support, or reframe traditional philosophical arguments. - Critically Evaluate the limits of both "armchair" philosophical reasoning and purely descriptive scientific data in addressing normative ("ought") questions. - Synthesize insights from both fields to develop more nuanced and empirically-grounded positions on complex human issues.

The course will take an intersectional approach to why we commonly find an underrepresentation of women in the "science" fields. The course will begin by addressing our own unconscious biases and stereotypes, and question the research behind male and female differences. We will cover the brief history of the underrepresentation of minorities and explore research articles on why and how women and other minorities have been excluded from STEM fields. We will primarily discuss how representation and the sense of belonging are important for diversity in the sciences to flourish. Students will be expected to read scientific articles, discuss them in class, and apply those concepts to examine their own social environments.

The course will take an intersectional approach to why we commonly find an underrepresentation of women in the “science” fields. The course will begin by addressing our own unconscious biases and stereotypes and question the research behind male and female differences. We will cover the brief history of the underrepresentation of minorities and explore research articles on why and how women and other minorities have been excluded from STEM fields. We will primarily discuss how representation and the sense of belonging are important for diversity in the sciences to flourish. Students will be expected to read scientific articles, discuss them in class, and apply those concepts to examine their own social environments.