Computer Science

This course covers various aspects of multi-core programming. Topics include programming models for multi-threading (Pthread), GPUs (CUDA), and the theoretical backgrounds behind them. Students also implement and optimize various emerging applications such as matrix multiplication, reduction, and deep learning kernels.

Through this course, students will learn basic optimization knowledge, be able to formulate a practical problem into a solvable optimization model, solve the problem using existing commercial or open source software or design their own algorithms to solve the problem.

This course mainly includes the introduction of convex analysis basics, linear and linear cone programming, optimal condition Lagrange duality, some basic optimization algorithms and their applications in finance, signal processing, machine learning and statistics.

This course instructs on python programming from a digital humanities perspective. It begins with the basics using interactive notebooks that require no installation. First, the course covers the basics of programming such as data types, loops, and variables. Later it explores and solves language-based and digital humanities problems using new programming skills and natural language processing (NLP) tools. Python 3 will be used in this course.

This course introduces the issues of digital ethics and data privacy faced by individuals and organizations. It covers the ethical principles governing the behaviors and beliefs about how we use technology, and how we collect and process personal information in a manner that aligns with individual and organizational expectations for security and confidentiality. It addresses challenges in balancing technological desirability with social desirability while developing digital products and services, including Professional Ethics, Computing for Social Good, Digital Ethics by Design, Digital Intellectual Property Rights, Automation and Autonomous Systems, and Artificial Intelligence Ethics and Governance.

This course introduces the mathematical formalism of quantum information theory. Topics include a review of probability theory and classical information theory (random variables, Shannon entropy, coding); formalism of quantum information theory (quantum states, density matrices, quantum channels, measurement); quantum versus classical correlations (entanglement, Bell inequalities, Tsirelson's bound); basic tools (distance measures, fidelity, quantum entropy); basic results (quantum teleportation, quantum error correction, Schumacher data compression); and quantum resource theory (quantum coding theory, entanglement theory, application: quantum cryptography). 

This course explores research issues in the newly emerging field of mobile computing. Many traditional areas of computer science and computer engineering are impacted by the constraints and demands of mobility. Examples include network protocols, power management, user interfaces, file access, and security. 

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.

The Individual Research Training Senior (IRT Senior) Course is an advanced course of the Individual Research Training B (IRT B) course in the Tohoku University Junior Year Program in English (JYPE) in the spring semester. Though short-term international exchange students are not degree candidates at Tohoku University, a similar experience is offered by special arrangement. Students are required to submit: an abstract concerning the results of their IRT Senior project, a paper (A4, 20-30 pages) on their research at the end of the exchange term, and an oral presentation on the results of their IRT Senior project near the end of the　term.

This is a special studies course involving an internship with a corporate, public, governmental, or private organization, arranged with the Study Center Director or Liaison Officer. Specific internships vary each term and are described on a special study project form for each student. A substantial paper or series of reports is required. Units vary depending on the contact hours and method of assessment. The internship may be taken during one or more terms but the units cannot exceed a total of 12.0 for the year.

This course provides a study of intellectual property rights and issues related to artificial intelligence including data technology and software. It covers legal and ethical issues related to artificial intelligence systems, software and big data utilization, etc.; intellectual property rights in AI related technology; fundamental principles and basic terms of intellectual property rights (e.g., patents, copyrights, trademarks, trade secrets, etc.) applicable to AI industry; the basics of technology transaction agreements including open source software license and other license agreements; and discusses various other social, ethical, legal and/or policy issues related to AI technology.