Computer Science

The advancement of information technology (IT) has rapidly changed our way of life. While IT encompasses technologies such as television and telephones, it commonly refers to computers and computer networks. 　　
　　
This course is intended for students with no or very little background in computers and will provide the basics of computers and computer networks. The students will acquire knowledge about basic software that would be required in an academic setting (i.e., word processing, spreadsheet, and presentation software). In class and homework exercises will allow students to get a hands-on experience with various software. 　　
　　

This course provides research training for exchange students. Students work on a research project under the guidance of assigned faculty members. Through a full-time commitment, students improve their research skills by participating in the different phases of research, including development of research plans, proposals, data analysis, and presentation of research results. A pass/no pass grade is assigned based a progress report, self-evaluation, midterm report, presentation, and final report.

A research project that assigns students to expert professors in their proposed research topic. The course takes students' research capabilities to a more professional level. This can be most closely compared to what is called a supervised research project in the USA.

The increasing reliance upon technological innovation, from pervasive digital computing in everyday smart phones and smart infrastructures to new forms of intelligent materials and pharmaceutical augmentation, is changing the nature of human-technology relationships. This course introduces the relationships among human-technology interface (HTI), human-machine interface (HMI), and human-computer interface (HCI), which have been rapidly developed during the past decade. The course teaches current design examples and theories of HTI as well as how they reflect the future of HTI.

This course provides individual research training for students in the Junior Year Engineering Program through the experience of belonging to a specific laboratory at Tohoku University. Students are assigned to a laboratory with the consent of the faculty member in charge. They participate in various group activities, including seminars, for the purposes of training in research methods and developing teamwork skills. The specific topic studied depends on the instructor in charge of the laboratory to which each student is assigned. The methods of assessment vary with the student's project and laboratory instructor. Students submit an abstract concerning the results of their individual research each semester and present the results near the end of the program.

This course discusses the basic concepts and methods of information retrieval including capturing, representing, storing, organizing, and retrieving unstructured or loosely structured information. 

The principles covered include caching in order to overcome latency; pipelining to increase processor utilization; Multi-Threading and Multi-Core principles, along with potential structures, and challenges such as memory coherency and consistency.

This course addresses a number of topics in computer and network security. Topics include memory errors, Web, network, countermeasures, and pointers to research papers. The course prepares students to identify software vulnerabilities, shows how to address these, and introduces how vulnerabilities are exploited through malware.

This special lab course nurtures international students' creative competency by offering them opportunities for learning in communities of research practice. The student's supervisor arranges the research topic. Students give three oral presentations during the study period. In the presentations, students integrate ideas and analyses on laboratory results into creative and academically coherent work. FrontierLab program coordinators and supervisors attend and evaluate the final oral presentation.

Data science and machine learning are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Good data science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods. This course extends the foundation of probability and statistics with an introduction to the most important concepts in applied machine learning, with social science examples. It covers the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods covered.