Computer Science

This course offers an introduction to HTML language for building websites from local devices. Each session includes one practice project.

The development of suitable models for describing dynamical systems is a central problem within automatic control, and it is critical for the development of robust and high-performance control laws. When relationships between physical quantities are not fully known, then models and the control laws may instead be generated by measurement data, through system identification, machine learning, or adaptive control. The purpose of the course is to teach the basic principles of how this is done. The first part of the course is devoted to adaptive control and system identification for systems with several input and output signals. The focus is on state-space models and methods for generating these, including grey-box identification. The course describes iterative methods for learning, as well as model reduction for the purpose of reducing the dimension of the state space. The second part of the course is devoted to reinforcement learning. This includes the theory of dynamic programming and various approximate methods thereof. Policy iteration is explained, as well as discrete and continuous path planning. The third part of the course deals with the usage of complete components for the purpose of control, for instance, sensors that have been developed using machine learning.

The course gives an insight into how functional programming often offers a possibility to write shorter and easier-to-understand programs than using the traditional imperative or object-oriented approaches. Course content includes the philosophy of functional languages, the programming language Haskell, language constructs and idioms, higher-order functions, lazy evaluation and infinite data structures, monads and monadic computations polymorphic type systems and type classes, and type analysis and type inference.

This course is an application of cognitive science that discusses the history of human-computer interaction and the future direction of development. Especially with the advent of the Web 3.0 era, the social and emotional development of human beings in the computing environment Interaction, cognitive processes, etc. are changing greatly, and the environment of new mankind such as autonomous driving, smart city, twin world, artificial intelligence and cognitive transformation are redefining the industry, productivity, social relationships and values of the future. This course is based on changes in the computing environment and focuses on the transformation and development of human cognition, especially the development of creativity.

The technological and physical basics of Brain-Computer Interfacing will be elaborated. It covers the path from the (electrical) activity of single neurons and networks via the volume conduction of the human head. At the end of the class, students will know the essential physical background of Brain-Computer Interfacing (BCI). They will understand the pathway from the activity of single neurons to the signal of the electroencephalogram (EEG) They will be capable of programming simulations of the electrical properties of the human head as well as simple neural and neural network models.

The course is intended to introduce some basic formal concepts and terminology pervading all areas of computer science, and to establish a common lexicon, including notational conventions and nomenclature, that subsequent courses can build upon. This includes an introduction to abstract set theory, relations, functions, ordered sets, Boolean algebra, logic, and proof techniques, as well as structures such as graphs and trees. Furthermore, the course discusses basic algorithms on graphs, an introduction to combinatorics, some fundamental proof strategies, and basic order structures such as lattices and complete partial orders (CPOs).

 

The lecture covers elementary concepts in machine learning and their application on real data with a special focus on methods that are simple to implement. The course alternates lectures and practice sessions. In the practice sessions, students implement and apply machine learning algorithms on real data in Python. Topics include: supervised learning (linear regression techniques, linear classification, kernel based regression), unsupervised learning (principal component analysis, clustering), and model selection.

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.