Students will explore and debate questions about human experience from multiple perspectives as they discover influential works from cultures around the world, both ancient and modern. Students will develop critical approaches and analytical skills as they exchange ideas with peers, ask questions, and respond to works in different genres through a variety of expressive modes. The course will focus on four fundamental themes: love, identity, death, and the meaning of life.

Students will explore and debate questions about human experience from multiple perspectives as they discover influential works from cultures around the world, both ancient and modern. Students will develop critical approaches and analytical skills as they exchange ideas with peers, ask questions, and respond to works in different genres through a variety of expressive modes. The course will focus on four fundamental themes: love, identity, death, and the meaning of life.

ゲノム研究の進展により生命科学は仮説駆動型からデータ駆動型の科学に変貌しつつある。このような科学を推進するには、多種多様で複雑なデータや知識を計算機でうまく扱えるようにすることが不可欠である。本講義では、その基礎となる理論や技術について解説する。 With the advancement of genome research, life science is transforming from a hypothesis-driven to a data-driven science. To promote such science, it is essential for computers to be able to successfully handle a wide variety of complex data and knowledge. In this lecture, the underlying theories and techniques will be explained.

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.

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.

The course will take an intersectional approach to why we commonly find an underrepresentation of women in the Science, Technology, Engineering and Mathematics (STEM) 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. Students will be expected to read scientific articles, discuss them, 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, Technology, Engineering and Mathematics (STEM) 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. Students will be expected to read scientific articles, discuss them, and apply those concepts to examine their own social environments.

人間の認知と感情に関して、その特徴や背後にある心理・神経過程、社会行動との関わり、個人差、エイジングなど、多様な最近の学術文献を用いた演習を行う。心理学全体に関わるトピック（新しい方法論など）についても適宜取り上げる。本演習を通じて、心理学の諸分野・関連分野の研究動向に広く接し、専門の枠を超えて理解・評価される研究とはどのようなものかについて考える。

研究や専門は本郷でと思っていませんか？駒場IIキャンパスに、世界が注目する若い研究室（https://www.lsbm.org/*****）が集まっているのを知っていますか？ 本ゼミは、駒場IIキャンパスで進む最先端バイオテクノロジー開発や生命科学研究の内容を、学部生の皆さんに伝え、実際の研究に関わっていくきっかけとすることを目指しています。４月26日（土）12時〜15時に、駒場IキャンパスのKOMCEE West (B1F-001)レクチャーホールにて開催される「駒場II 生命科学シンポジウム」に参加してください（集中講義）。冒頭に、本全学ゼミのガイダンスを行い、その後、駒場IIキャンパスで行われている研究・開発内容や、学生の関わり方に関して発表します。 さらに同日シンポジウム後の15時〜18時に、駒場IIキャンパスの生命科学・技術系の研究室合同でポスター発表会（KOMCEE East B1F ホワイエ、駒場Iキャンパス）があります。希望者は参加して各研究室の教員や研究者・学生と交流し、より深く学ぶ機会もできます。また、担当研究室に個別に連絡（email）をして各研究室に独自に見学・体験（実践的な研究に携わる）することもできます。 同シンポジウムで紹介される先端バイオテクノロジー開発・生命科学研究に関して、興味のある研究室の研究内容に関して、レポート課題（https://www.sadaotalab.net/***** https://sadaotalab.com/***** に掲載）を提出してください。 シンポジウム後にポスター発表会に参加してより深く学ぶことが可能です。担当研究室に個別に連絡（email）をして各研究室に独自に見学・体験（実践的な研究に携わる）することも可能です。 本授業は出席と提出するレポートの内容で成績評価を行います。ガイダンスは、４月に対面で行います。 【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、先端科学技術研究センターのメンバーを中心とするバーチャルな十以上の研究室集合体です。工学・理学・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 course aims to cultivate internationally competitive young researchers equipped with literacy and competency to become future leaders in industry and academia. The course deals with multidisciplinary application skills and the in-depth research in specialized fields so that students accomplish the ability to work in a broader spectrum and apply one’s skills to a multidisciplinary setting. The topics of the course include medical and biomedical robotics, medical high-tech industries, disease prevention, health care system, science technology and industrial policy, energy technology, and health security and community resilience.