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.

本ゼミは、駒場IIキャンパスにおいて行われている最先端の生命科学やバイオテクノロジーの研究開発の内容を、学部生の皆さんに伝え、実際の研究に触れ、関わっていくきっかけとすることを目指しています。駒場IIキャンパスにおいて行われている先端生命科学研究に関しては、システム生物医学ラボラトリーズ（Laboratories for Systems Biology and Medicine: LSBM）のホームページ（https://www.lsbm.org/*****）に掲載しています。 【先端研とは】 学部生のみなさんが主に学ぶ駒場Ⅰキャンパスから歩いてすぐの所、駒場Ⅱリサーチキャンパスにある先端科学技術研究センター（先端研）は、「学術の発展と社会の変化から生じる新たな課題へ機動的に挑戦し、人間と社会に向かう先端科学技術の新領域を開拓することによって、科学技術の発展に貢献することを目的とする」研究所です。 【LSBMとは】 LSBMは、次世代生命医科学を開拓すべく有機的に協働する、バーチャルな十以上の研究室集合体です。主に先端研のメンバーを中心として理学・工学・情報科学・医学の研究者が、積極的なコラボレーションを展開しています。 This seminar aims to inform undergraduate students about the cutting-edge life science and biotechnology research and development taking place at the Komaba II campus, and to provide them with an opportunity to experience and become involved in actual research. Information about the advanced life science research being conducted at the Komaba II campus is posted on the Laboratories for Systems Biology and Medicine (LSBM) website (https://www.lsbm.org)./***** The Research Center for Advanced Science and Technology (RCAST), located on the Komaba II Research Campus, a short walk from the Komaba I Campus where most undergraduate students study, is dedicated to "contributing to the advancement of science and technology by taking on new challenges arising from academic development and social change, and by exploring new areas of advanced science and technology for the benefit of humanity and society. LSBM is a virtual group of more than ten laboratories that collaborate organically to pioneer the next generation of biomedical science. LSBM is a virtual group of more than ten laboratories that collaborate organically to pioneer the next generation of biomedical science.

医療・介護の分野における医薬品情報の重要性について、モノとヒトなど様々な視点から理解を深め、育薬という考え方を身に着ける。

What is for a person to live a good life, that is, a life that it good for the person? It is usually said that such a good life consists in the happiness – or well-being – of the person. But what, then, is well-being? Philosophers have given various answers to this question, and raised challenges to the coherence or utility of the notion itself. This course will survey the major philosophical theories of well-being.

What is for a person to live a good life, that is, a life that it good for the person? It is usually said that such a good life consists in the happiness – or well-being – of the person. But what, then, is well-being? Philosophers have given various answers to this question, and raised challenges to the coherence or utility of the notion itself. This course will survey the major philosophical theories of well-being.

What is for a person to live a good life, that is, a life that it good for the person? It is usually said that such a good life consists in the happiness – or well-being – of the person. But what, then, is well-being? Philosophers have given various answers to this question, and raised challenges to the coherence or utility of the notion itself. This course will survey the major philosophical theories of well-being.

What is for a person to live a good life, that is, a life that it good for the person? It is usually said that such a good life consists in the happiness – or well-being – of the person. But what, then, is well-being? Philosophers have given various answers to this question, and raised challenges to the coherence or utility of the notion itself. This course will survey the major philosophical theories of well-being.

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。

This course explores the persistent challenge of gender-based data disparities and their far-reaching consequences for society. Across various domains, from health and education to economic participation and political representation, the lack of gender-disaggregated data hinders our ability to understand and address the unique needs and experiences of women, girls, and gender-diverse individuals. Throughout the course, students will engage in discussions, readings, and a hands-on project that involves analyzing publicly available gender-segregated data. This will allow them to develop a deep understanding of the gender data gap and its implications, as well as practical skills in data analysis, visualization, and policy advocacy. This class will be taught in English and students will be expected to speak in English and complete assignments in English.