This course is designed to provide students with the essentials of biology. Biology = Science of Life. What are we? What is Life? What do living things have in common? What makes us, us? Non-native English speakers are welcome. Consider taking this course if you want to learn Biology in English and interact with English speakers.

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.

脳は様々な領域同士が結びつき，巨大な情報ネットワークを構成している．近年，脳の情報ネットワークは，人の認知機能の生物学的な基盤の解明に大きく貢献している．本授業では，脳のネットワークを中心的なテーマとして，脳情報の解読（デコード・エンコード），ニューロフィードバック，脳刺激，神経細胞活動記録など，認知神経科学の関連分野の文献を輪読する．最近出版された英語論文（展望・原著）を購読し，その解析の原理を理解し，受講者自身が興味を持つテーマへの応用を考察してもらう．受講者には，割り当てられた発表だけでなく，積極的な質疑応答が求められる．

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 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 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.

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.

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.

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.