Computer Science

In this course, student learn to process XML (with XSLT and Java), to model data with XML (XML native, RDF), and to query XML data (XQuery). The course teaches many concepts of data modelling and knowledge representation that are beyond the syntactic issues of XML or RDF. The knowledge students acquire in the course is fundamental to the many data design and data analytics tasks occurring in todays IT and business landscapes. The second part of the course is dedicates to advanced DB concepts including active databases, mobile databases, spatial and temporal databases, triggers, performance tuning, distributed databases, and indexing and query optimization. The third part of the course covers the modern, agile world of data processing: NoSQL. It is about the processing of semi-structured data, transforming data streams into formats (triplets, JSON) to be processed by new DB systems (e.g. MongoDB, CouchDB). 

The course provides basic knowledge in the field of artificial intelligence and machine learning for applications in medicine and health. The course covers the chain from medical databases via algorithms to regulations and requirements for diagnostic software.

This course is about randomness as a resource in algorithms and computation. The course introduces basic mathematical models and techniques and applies them to the design and analysis of various randomized algorithms. Students also cover a variety of applications of probabilistic ideas and randomization in several areas of computer science.

This course provides an introduction to databases. Topics include: information systems, modeling methodologies, and management of semi-structured and complex data; relational database including design of a database and query languages; NoSQL databases.

This class teaches basic principles, guidelines, tools, and practices of human computer interaction. It covers a broad range of issues starting with human cognitive and perceptual capabilities, 2D interfaces, 3D and multimodal interfaces, interfaces for web and mobile devices, and usability and evaluation methods. The course will emphasize practical applications and thus require students to carry out many UI design and evaluation projects. The lectures will aim to use as many case studies as possible.

Recommended prerequisite: C/C++ Programming 

Programs written in programming languages ​​such as C or Java are translated into assembly language or machine language programs by a special software called a compiler. This course explains the basic concepts and formalization of programming languages, explaining how the programs we usually write are executed inside a computer and how the compiler is configured for that purpose. Compilers can generally be divided into two parts: a front end and a back end. This course focuses on the front end, which comprises of three parts: lexical analysis, syntactic analysis, and semantic analysis.  

This course is highly applied in nature with two important database topics, namely, traditional relational databases and SQL, as well as non-traditional databases and NoSQL queries. Students are expected to know basic programming using Python as a prerequisite. In this course, students learn, understand, use, and apply the principles and technologies of data management to business analytics. Doing so creates two benefits - (1) students understand the complexities of enterprise business analytics much more deeply and have a set of principles and techniques to apply to wrangle these complexities; and (2) students become technically proficient and comfortable in data management technologies (like SQL and NoSQL), so they can implement these principles on their own. In this course, students gain a much broader appreciation for real-world enterprise analytics - how data management, data science/analysis and data visualization come together to build analytics capabilities for organizations. This appreciation strengthens students’ abilities to tackle the organizational challenges associated with analytics. Finally, students become more robust technically, and develop keys technical skills needed in all business analytics professionals.

This course examines the fundamental concepts, methods and techniques of usability engineering. 

This is an introductory course on modern Artificial Intelligence designed for Keio University. The course is composed of two parts taught in consecutive semesters: material introduced in part A forms a foundational basis for part B (this course), which develops these ideas further and introduces a selection of more recent results based on guided reading of relevant publications. The two courses taken in sequence form a coherent introduction to neural Artificial Intelligence. The first course focuses more on theory and fundamental concepts, with implementation of basic techniques in Python. The second course (this one) aims to cover more practical engineering topics using modern practices, as well as introducing some of the most influential recent advancements based on a selection of research papers. Part B of the course also introduces 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 introduces students to the field of Artificial Intelligence, focusing on Deep Neural Information Processing Systems. Since this is a rapidly developing field, it focuses on the most important trends and core ideas. The course 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; it then discusses current neural architectures and algorithms and introduces modern perspectives. Completion of this course leads to an appreciation and understanding of neural AI systems and anticipation of future developments in research and applications of AI, and Deep Learning in particular. In addition to theory, there will be emphasis on programming skills in Python. The course will implement deep neural AI systems and train students on standard data sets.

It is recommended that students complete both courses (A and B) in sequence. However, it is possible to take this course as a standalone, after consulting the instructor during the first lecture. In such cases, students should review the material from part A in their own time, as this course builds on previously introduced concepts.