Computer Science

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

 

Main topics include the design of parallel computers and parallel programming models; shared memory architecture; message passing and distributed memory architecture; parallel programming of computer clusters using MPI and multicore programming using OpenMP; parallel algorithms for sorting, searching, linear algebra, and various graph problems.

This course introduces students to the fundamental theory and practice of the social, political, legal, and ethical implications of computer technologies in Japan and abroad. Through in-class activities, group assignments, and reflection work, students will gain a basic understanding of essential concepts, modern and historical cases, and guidelines for best practice. Key concepts include AI bias; privacy in the social media era; personal data and digital behavior tracking; vectors of misinformation; stereotypes in design, digital inclusion, and more. The main objective is to inform and encourage critical thinking in students who will be playing key roles in deciding, creating, marketing, governing, and disseminating computer technologies in Japan.

Typically, the first class each week will introduce a new topic, with interactive activities (e.g., hands-on demos, brainstorming, quick activities), individual reflection, and group discussion. Students will be given a homework assignment to be completed before the second class that week. That second class will start with a discussion of the homework and introduce the next topic for that week. Students will be expected to complete readings from the text and/or other sources before the next week of classes. Attendance is taken randomly in every class.

This course is to systematically introduce the knowledge related to the ethics and governance of artificial intelligence technology, improve students' moral sensitivity to the ethical issues of artificial intelligence, and jointly explore the possible strategies of AI governance.

The course will first systematically introduce the technical principles and development history of artificial intelligence, discuss the technical nature of artificial intelligence from a philosophical perspective, and analyze the safety and ethical issues of artificial intelligence. On this basis, it will further explain the path and strategy of artificial intelligence safety and ethical governance from three aspects: technical norms, ethical guidance and institutional regulations.