This is an introductory course on mathematical methods for public policy analysis, designed for GraSPP students without a strong background in mathematics. Students from non-economics, non-engineering, or non-science majors are especially welcomed. The course helps students develop a solid mathematical foundation, enabling them to apply essential mathematical techniques to public policy issues. It is structured into four parts: 1. differential calculus, 2. linear algebra, 3. multivariate calculus and constrained static optimization, 4. dynamic optimization and linear dynamic systems.

This course aims to raise awareness on the critical role of space technology in our daily lives and the important implications it has on domestic policymaking and international relations. The first part of the course, titled “Space and Society”, provides an overview of major space technologies and their applications. It then demonstrates how space assets have become a critical infrastructure on which advanced societies are over-reliant, and the hazards and threats that they are facing. The second part of course delves into political science and international relations. It addresses the various approaches to space policymaking, the structure of international relations in outer space and the major challenges ahead for the space sector. This course will be a part of the global effort to map threats and consequences for space sustainability conducted by the United Nations Institute of Disarmament Research.

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.

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.

This course aims to raise awareness on the critical role of space technology in our daily lives and the important implications it has on domestic policymaking and international relations. The first part of the course, titled “Space and Society”, provides an overview of major space technologies and their applications. It then demonstrates how space assets have become a critical infrastructure on which advanced societies are over-reliant, and the hazards and threats that they are facing. The second part of course delves into political science and international relations. It addresses the various approaches to space policymaking, the structure of international relations in outer space and the major challenges ahead for the space sector. This course will be a part of the global effort to map threats and consequences for space sustainability conducted by the United Nations Institute of Disarmament Research.

This course aims to provide first-time learners of economics with solid foundations for understanding introductory macroeconomics. To this end, students will learn basic concepts in macroeconomics, such as GDP, unemployment, inflation, exchange rates and, based on them, how to use workhorse macroeconomic models to analyze consequences of a variety of economic shocks and policy interventions. By taking this course, students are expected to have a deeper understanding of how the macroeconomy functions and be able to critically assess economic news and conventional policy measures.

現代の科学技術の諸分野において，扱う現象を数式を用いて表し（数理モデル化），得られた数理モデルの解を解析するというアプローチは必須のものとなっている．ところが，数理モデルの解（厳密解）を陽に書き下すことは不可能なことが多く，コンピュータを用いた数値計算で近似解を得るのが有力な選択肢となる．本講義では，数値計算を行う対象として最も基礎的な問題（連立一次方程式・非線形方程式・関数の補間・積分・常微分方程式）を取り上げ，数値解析の手法や考え方を紹介する． 数値解析の立場では，特に(1)「アルゴリズム導出のアイデア」，(2)「アルゴリズムの数学的正当化」の2点が重要である．上に挙げた基礎的な問題ですら，厳密に解を求める手軽な公式がない・公式があっても数値計算には現実的ではないといった状況に直面するため，元の問題に対して「近似」や「離散化」といった操作を施し，コンピュータで計算可能な公式（アルゴリズム）を得る．このとき，最終的なアルゴリズムに到達するための近似や離散化のアイデアを理解するのが(1)の段階である．さらに，（一般に同値変形でない）近似や離散化の操作を行っても本当に問題ないのかということをきちんと考察するのが(2)の段階である．いずれのステップにおいても，数学的議論が理論の土台として重要な役割を果たしていることを解説する． 講義で学んだアルゴリズムをプログラムとして実装し，実際に数値計算を行ってその有効性を確かめる実習を行うことが望ましいが，そのために計算数理演習の履修もすすめる．なお，進度に応じて講義内容を変更する場合がある．

ビッグデータの時代と言われている。近年、データの計測およびストレージ技術の発達とともに、大規模データから適切に情報抽出し、それを意思決定に活用することが必須のリテラシーとなっている。いっぽうデータの形式と対応する解析法の変化は著しく、新しい方法を正しく利用するために、普遍的な統計科学の原理を理解することが重要である。基礎となる統計数理とともに、具体的な統計解析手法とその運用を、統計ソフトウエアによるデータ解析実習を通じて習得する。 　統計データ解析Ⅱでは、統計ソフトウエアRの説明の後、高次元大規模データに潜む相関構造を発見し計量する多変量解析、および時系列データの基本的な解析法を学ぶ。統計手法の運用とデータハンドリングを実習することに加え、微分積分学、線型代数学等の前期課程数学と連携し、数理科学的側面を意識しながら、実験を介して統計手法の合理性と体系を感得する。 It is said that we are in the era of Big Data. With the recent development of data measurement and storage technologies, it has become essential literacy to properly extract information from large-scale data and utilize it for decision-making. However, the format of data and the corresponding analysis methods have changed remarkably, and it is important to understand the essential principles of statistical science in order to properly use new methods. Students learn basic statistical mathematics as well as specific statistical analysis methods and their application through practical training in data analysis using statistical software. In Statistical Data Analysis II, after explaining the statistical software R, students learn multivariate analysis for finding and weighing correlation structures hidden in high-dimensional large-scale data, and basic analysis methods for time series data. In addition to the practical training in the operation of statistical methods and data handling, students will gain a sense of the rationality and systematics of statistical methods through experiments, while being aware of the mathematical scientific aspects in conjunction with undergraduate mathematics such as differential and integral calculus and linear algebra.

Basic knowledge on fishery economics will be reviewed through lectures using various academic papers. The following topics will be covered: 1) The state of fisheries 2) Fisheries biology and stock assessment 3) Economics of overfishing 4) Right-based fishery management 5) Small-scale fishery 6) Seafood consumption and health 7) Eco-labels 8) Fishbanks 9) Fishers' microbehaviors

地球外物質に注目し，太陽系の起源と進化に関する物質科学の基礎と最先端を理解する． Lectures on the origin and early evolution of the Solar System through analysis of pristine extraterrestrial materials, laboratory experiments, and Solar System exploration and on basic physical chemistry and material science.