Computer Science

In this course and through the DAMS Lab group (FG Big Data Engineering), students learn how to conduct research in areas of data engineering, data management, and machine learning systems. Students review scientific literature in these areas as well as how to design, implement, and evaluate prototypes. The lab group offers this project on large-scale data engineering. The course includes tasks in a wide range of components of data management and machine learning systems. Students will have the opportunity to make meaningful contributions to free open-source projects.

In this course, students demonstrate independence and originality in order to plan and organize a large project over an extended period, and to put into practice prior engineering knowledge, skills, and research methods that they have learned throughout the course. Students demonstrate their ability to apply previously taught knowledge and skills to a substantial problem in computing; conduct an independent investigation and apply cutting-edge research, methods, and thinking appropriate to the problem; present complex technical material orally to a mixed audience; and exercise scientific writing skills by way of a substantial written report, summarizing their findings.

In this course, students advance their knowledge of data-structures and algorithms to data-processing algorithms and  applications. They acquire theoretical and practical knowledge of data processing systems design and implementation for correct results and (close-to) optimal performance. Students learn how Database Management Systems (DBMSs) optimize query performance, and understand Data Processing System tuning. Finally, students explore challenges and opportunities of cloud-native Data Processing Systems, as well as the research directions such as Big Data or data management on modern hardware.

This course empowers undergraduate students in the College of Natural Sciences with essential knowledge in programming and artificial intelligence. Regardless of their specific majors, students gain foundational insights into computer science, computational science, statistics, and deep neural networks. This course equips students with practical skills that can be directly applied to scientific challenges. Through a combination of theory and practical exercises, this course offers students the opportunity to tackle real-world problems and work with data using artificial intelligence techniques. Students who possess basic computing and programming skills gain an understanding of how artificial intelligence and programming are applied in various subfields of natural sciences, fostering their ability to utilize these skills in future research endeavors. 

This six-week summer course provides individual research training through the experience of belonging to a specific laboratory at Tohoku University. Students are assigned to a laboratory research group with Japanese and international students under the supervision of Tohoku University faculty. They participate in various group activities, including seminars, for the purpose 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 this program.

This course examines network and web security broadly from the network to the application layer. The emphasis of the course is on the underlying principles and techniques, with examples of how they are applied in practice. Students study the themes and challenges of network and web security, and the current state of the art. They develop a critical approach to the analysis of network security and web application security, and learn to bring this approach to bear on future decisions regarding security.

This course will give you a beginner-friendly introduction to the Theory of Computation. The Theory of Computation seeks to categorize computational problems based on their inherent difficulty, measured by the resources (primarily time and space) needed to solve them. It also aims to explore the relationships between different problems, such as determining whether problem X is not harder than problem Y. This course will help you gain a rigorous understanding of computation, including its definition, possibilities, and limitations. Topics include finite automaton and regular language, Turing machine and its variants, computability, and complexity theory.
 

This special topics data sciences course covers up-to-date research trends in prompt engineering and prompt engineering interactions with large scale language modeling. The course examines how prompt engineering significantly impacts the effectiveness of LLM-based applications and interactions with generative AI. 

Academic researchers, industry vendors, and practitioners have proposed many practical techniques and guidelines for building LLMs or applications on LLMs. In this course, students review concepts and techniques that can be used to guide the model in how to behave in a way that is aligned with users' preferences or perform a specific task. 

Topics include basic concepts of LLMs, Foundation model vs custom model, Fine tuning vs prompt tuning, Methods of prompt engineering, Agentic workflow, Integrating local preparatory knowledge bases, and more. 

This course explores the main algorithmic design paradigms and teaches students to apply algorithmic techniques to practical and unseen problems. Students quantitatively analyze the performance of algorithms. They also model the mathematical structure of computational tasks and apply the right algorithmic tools on them, and develop their algorithmic thinking and problem solving skills.

This course explores the underlying principles of several cutting-edge topics in machine learning and deep learning, including adversarial attacks, deep metric learning, generative models, information theory, and reinforcement learning.  

In addition, the course examines the end-to-end construction of modern large language models and practices core concepts by implementing them. Students engage in coding assignments and team projects using GPU-enabled computer servers to test original ideas. 

Topics include concepts and history of deep learning, backpropagation techniques such as stochastic gradient descent, initialization techniques, regularization techniques such as drop out, convolutional neural networks (CNN), CNN architectures, visualization of CNN, recurrent neural networks (RNN), RNN applications, and other applications including reinforced learning. 

To emphasize practical skills to implement deep learning algorithms, programming-related lectures and lab sessions are included. The most important/popular language, Python, will be covered and a Python math library called Numpy is also taught with lab sessions. Advanced deep learning algorithms are implemented in Tensorflow library, which is introduced as well including relevant lab sessions