Computer Science

This is a cross domain course which students are divided to two groups. One group focuses on Big Data processing needs, analytics, machine-learning and recommendation systems. The other emphasizes compilers and their contexts, be it Android compilation or Big Data languages. This is crucial especially today; Benefitting from Moore's Law, the main abstraction level in Computer Science has shifted higher rapidly. In comparison, Taiwan's industry has been buried in the hardware, drivers, and benchmarking game. Both groups are taught by an author of Big Explorer, Android Virtual Machine and RenderScript Engine (Google). The course also includes a mini-hackathon.

The course covers the basic principles, methods, and application of database technology. It examines existing database management systems and software development tools, and the core implementation technology of database management systems, database model design, and database application system development principles. Topics include basic concepts of database system, operation theory of relational model, SQL language, standardized design theory, database design, database storage structure, database query processing process, database management system implementation technology, database security, graph/sequence data management technology, NoSQL database, and cutting-edge paper reading.

This is an introductory course on modern Artificial Intelligence designed for Keio University. It focuses predominantly on theory and fundamental concepts, with implementation of basic techniques in Python. Depending on the level of the students and time constraints, it may also cover more practical engineering topics using modern practices, as well as some of the most influential recent advancements based on a selection of research papers. Additionally, the course also covers some topics in more depth based on the interests of the instructor. One of those topics is Natural Language Processing (NLP) in the era of Deep Learning, as well as advanced methods in representation learning.

This course focuses on Deep Neural Information Processing Systems. As a rapidly developing field, the course centers on most important trends and core ideas, as it is impossible to cover all recent developments in a single course. It follows historical trends in AI with a focus on neural networks, seeing how the current ideas emerged out of decades of research in the field.  Then, the course discusses current neural architectures and algorithms, while introducing modern perspectives. After completing this course, students are expected to have an appreciation and understanding of neural AI systems and anticipate future developments in research and applications of AI (especially Deep Learning). 

This course covers the basic grammar of Java language and its programming ideas, use Java language to implement algorithms, and learn the common design, development, testing techniques and application development techniques of Java language (including Internet development and mobile application development). This course contains the learning of Java language grammar, program structure, design method and testing technology; object-oriented programming design ideas; multi-threaded concurrent processing technology, Internet connection access processing technology, program fault-tolerant processing mechanism; Android application development.

This course examines the close integration of music art and the latest information technology, which leads to the revolutionary change of modern music creation, performance, appreciation and communication mode and the main scientific knowledge and technical principles behind it.

This course introduces mathematical tools required in the study of computer science. Topics include: Logic and proof techniques: propositions, conditionals, quantifications; relations and functions: equivalence relations and partitions; partially ordered sets; Well-Ordering Principle; function equality; Boolean, identity, inverse functions; Bijection; mathematical formulation of data models (linear model, trees, graphs); counting and combinatoric: Pigeonhole Principle, Inclusion-Exclusion Principle; number of relations on a set, number of injections from one finite set to another, diagonalisation proof: An infinite countable set has an uncountable power set; Algorithmic proof: An infinite set has a countably infinite subset; subsets of countable sets are countable.