While music is often touted as a “universal language” and thought of in terms of its positive functions—e.g., tool of aesthetic, spiritual, and/or intellectual expressions; formation of personal identities; building relationships and communities; mobilizing spirits and promoting actions; healing and rehabilitation; enhancing ambience and environment—it also involves various forms of inequity, exclusion, and exploitation in its creation, circulation, and consumption. This course examines the issues of diversity, equity, and inclusivity in musical practice and explores what it means to pursue justice in/through music. Through discussions of key concepts and assigned readings, field observations, and group projects, we will explore such topics as: class, race, and gender in musical practice; ability, productivity, and creativity in music; music and representation; advocacy and activism in/through music. Through the course, students will: - learn to think of music as not only as an artistic but also a social practice - understand various forms of exclusion, marginalization, and exploitation that occurs in musical practice - learn about examples of advocacy and activism for social justice in/through music - learn interdisciplinary approaches to the study of music, including American studies, cultural studies, ethnography, ethnomusicology, history, musicology, and sociology - engage music as an embodied social practice and analytically reflect on the experience

While music is often touted as a “universal language” and thought of in terms of its positive functions—e.g., tool of aesthetic, spiritual, and/or intellectual expressions; formation of personal identities; building relationships and communities; mobilizing spirits and promoting actions; healing and rehabilitation; enhancing ambience and environment—it also involves various forms of inequity, exclusion, and exploitation in its creation, circulation, and consumption. This course examines the issues of diversity, equity, and inclusivity in musical practice and explores what it means to pursue justice in/through music. Through discussions of key concepts and assigned readings, field observations, and group projects, we will explore such topics as: class, race, and gender in musical practice; ability, productivity, and creativity in music; music and representation; advocacy and activism in/through music. Through the course, students will: - learn to think of music as not only as an artistic but also a social practice - understand various forms of exclusion, marginalization, and exploitation that occurs in musical practice - learn about examples of advocacy and activism for social justice in/through music - learn interdisciplinary approaches to the study of music, including American studies, cultural studies, ethnography, ethnomusicology, history, musicology, and sociology - engage music as an embodied social practice and analytically reflect on the experience

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.

This course selects a specific theme from the broad field of machine learning and cover its fundamental concepts, the principles behind key algorithms, implementation, and applications to real-world problems. This year we focus on unsupervised learning. Unlike supervised learning, evaluating the results of unsupervised learning is not straightforward. Furthermore, the common definition that "unsupervised learning is learning with unlabeled training data" is not always accurate. We introduce diverse topics ranging from classical principal component analysis (PCA) and clustering methods to deep generative models. The course is roughly divided into two parts: the first part will delve into foundational and classical (yet still important) unsupervised learning methods, and the last part will deal with deep generative models.

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.

分野トップの講師陣による講義。異分野の基礎と先端について学び、系統的な幅広い知識を習得する。 Lectures by a diverse group of top researchers in their fields. From the basics to the state-of-the-art research. Develop a broad range of knowledge systematically.

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.

There have developed quite separate debates concerning, on the one hand, the Transformation of Warfare, and Revolution in Military Affairs on the other. Conventional knowledge is that while the former approach encompasses a vast range of scholarly analysis on how war in the contemporary era might have transformed in all spheres of economy, politics and society, the latter has focused on the impact of technological advancement in a narrower sense on warfare, especially the way in which warfare has been conducted or ought to be conducted. However, to discuss changes in the character of war in these quite distinct spheres, each with specific preoccupations with no interactions, is not very conducive to a meaningful generalization about the transformation of warfare itself. The purpose of this seminar is to consider the impact of modern and contemporary technological change, especially in the spheres of communications and media, on the character and conduct of warfare today, thereby bridging the two distinct spheres of interest. Some technologies are considered “disruptive”, such as AI and automation, but how do these newer technologies impact the nature and character of warfare today and into the future?

広く産学官にわたってグローバルに活躍するために必要な「俯瞰力」を養成することを目指す。物質科学の各分野について最先端の知識を修得し、自分の専門分野と周辺分野がどのように関連するか、あるいはし得るか、について深く考察するために、第一線で活躍する講師の方々にその分野の最前線を概観していただく。さらに、それらの講義を通して異分野間のコミュニケーションを円滑に進めるための具体的方法論を学ぶ。 This survey course is designed to enable students to develop the broad perspective that is required of global leaders working in and across industry, academia, and government. Students will gain knowledge and insight on advancements in each field of materials science research, given by leading researchers working on the frontline in those fields. This will allow students to consider how peripheral fields are related to their own area of expertise, and to consider the potential for forging bridges between related fields in the future. In addition, students will learn specific methodologies designed to facilitate smooth communication among different disciplines.