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.

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．

現在の人工知能技術が直面する課題や限界を乗り越えるため，今改めて人間の脳の機能と仕組みを深く理解することが極めて重要となっている．/ In order to overcome the problems and limitations of the current AI technology, a deeper understanding of human brain function and mechanism is now becoming important again. 次世代の人工知能は，何が起こるか事前に限定されておらず，常に変動する複雑な実世界で自律的に適切に振る舞う「動的実世界知能」と，人間の振る舞いや意図を理解し，人間の価値観や道徳に適合する人間中心の意思決定や行動を行う「人間的知能」を兼ね備える必要がある．/ The next generation AI should be endowed with "Dynamic Real World Intelligence" which can autonomously and appropriately behave in the dynamically changing complex real world full of non-predetermined events, as well as "Human Intelligence" which understands human behavior and intentions, exhibits human centered behavior and decision making that match human value and morals. 本講義では，上記課題に向けて，従来の人工知能が捉えていない生体型計算原理や脳機能に重点を置き，身体性，感覚運動ダイナミクス，認知発達，社会性，高次認知，意識，などに関する計算論的神経科学や構成論的科学，認知発達ロボティクス等の最新研究動向を基礎および最新の脳科学的知見を踏まえつつ学び，議論する．/ In this class, we study and discuss towards the above issues, focusing on biological computation principles and brain functions not yet captured by past AI. Referring to the basic and advanced brain science, the latest advancement in computational neuroscience, constructive science and developmental cognitive robotics on embodiment, sensory-motor dynamics, cognitive development, sociality, high-order cognition, consciousness, etc. will be presented.

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.

人間の発達は、胚の形態形成にはじまり、胎児や新生児に与えられた遺伝的要因と生得的な能力、自らを変化・発展させる内在的な機構、物理的環境や他者との相互作用により適応・学習していく機構などが複雑にからみあう現象である。本演習では、講義及び受講者による演習、原著論文講読、レポート作成、プレゼンテーションを行い、発達脳科学に関わる基本的概念を学ぶ。

本講義では、統計的パターン認識、機械学習と、ほかの関連する話題についての理論並びに実践について学ぶ。 This course will cover statistical pattern recognition, machine learning, and other related topics in the aspects of theory and practice.

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.