Computer Science

This course provides insight into how programs written in high-level language are implemented on a computer. It covers various elements of interpretation and translation of programming languages: lexical analysis, syntax analysis, type checking, interpretation, code generation, register allocation, and storage management. It reviews the basic methods for implementing these elements, including the use and operation of semi-automatic tools. In connection with lexical analysis and syntax analysis, the course demonstrates how descriptions that are convenient for people (respectively, regular expressions and context-free grammar) are transformed into automata that are convenient for machines. These transformations are the foundation for tools that can automatically produce lexical analyzers and syntax analyzers based on descriptions. In connection with the generation of intermediate and machine code, the course reviews how machine code can be generated on the basis of the syntactic structure of a program and presents different methods for optimizing code.

The course contains the learning materials, practices and case studies to develop the knowledge and skills of the students in the field of data science and its application in the real business/work world. The students learn how to apply analytical techniques and scientific principles to extract valuable information from business data for decision-making, strategic planning. This course covers practical contents of statistics, machine learning, information visualization, and data analysis techniques through python programming language and other tools.

Digital technology has fundamentally and dramatically changed the way people live; the way society develops, and, most importantly, it refined the fundamental nature of human civilization. We are spending more time and effort in borderless cyberspace but, at the same time, dealing with issues in a multicultural physical space. Technology and society co-evolve, thus making it essential to understand both in order to grasp the best opportunities and also prepare for the upcoming challenges that arise from the ever-increasing integration of technologies into our societies.

This course broadly covers issues related to emerging technology advancement and addresses its critical societal challenges such as privacy, cybersecurity, governance, media, business stability, law enforcement, justice, and new modes of the workforce, among others. The course also investigates Japanese internet governance as well as privacy protection rules in a global context for a better understanding of not just the Japanese, but the global trends in building healthy relations between technology and society. The course aims to educate students to think critically about approaches and possible solutions to the challenges in the physical and virtual domain.

 

This course emphasizes hands-on laboratory experience and teaches students research background, relevant theories, and basic laboratory techniques relevant to their field of study. Students formulate a research plan, implement it by conducting experiment-based research, and convey the results in scholarly presentations. Students submit a written research report at the end of the course.

In this course students acquire a broad knowledge base and develop analytical and critical thinking skills. Students actively participate in seminars, read assigned texts and research papers, and analyze research data. Students also discuss results obtained in their own experiments with peers and senior laboratory members.

In this course, students apply their background in linguistics to the field of machine learning. The course provides an overview of the machine learning sub-field of natural language processing. Students delve into mathematical/computer science aspects of the topic and learn about different types of machine learning, neural networks, how to work with data, and specific implementations to the field of linguistics. Students may complete a final coding project that relates to the field of linguistics. The course also covers philosophical/ethical aspects of the field, and students discuss issues like ChatGPT and its implications on higher education, the job market, and more. Because this course is in the linguistics department, there will be a heavy emphasis on syntax/semantics, and students should have a strong linguistic knowledge.  

This course studies how the internal structure of operating systems is designed and implemented for management of resources and provision of services. Topics include process and thread creation and management; communication in processes and threads; process synchronization and deadlocks; memory-management strategies; and protection and security. 

The course introduces computational approaches to model human behavior and social phenomena. Core concepts in computational social science are covered, such as observational studies (what types of data exist, possible biases, and how to use data for modeling), basic concepts and techniques for running experiments (asking vs. observing, natural experiments, simulations, validity, and generalization) and discuss key issues such as ethical considerations. The course has both a theoretical and a practical perspective, where you learn basic principles and also how to apply them in practice in three main areas: social network analysis; text analysis; agent-based modeling, and simulation.

This course examines the role of data abstraction to solve problems. It discusses data structures, their characteristics, and implementation in object-oriented programming language. Topics include: arrays; recursions; lists; batteries; tails; trees; binary trees; binary search trees; balanced search trees; functions and hash tables; heaps; sorting algorithms; algorithms in graphs. 

Database management systems are at the core of computer applications that need to store, manipulate, and query data. This course takes a deep dive into how modern database systems function internally, from studying their high-level design to understanding the underlying data structures and algorithms used for efficient data processing. The course covers a range of data management techniques from both commercial systems and cutting-edge research literature, enabling students to apply these techniques to other fields of computer science. This is the undergraduate version of INFR11199