Computer Science

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.

This course offers an introduction to data science. Topics include: introduction to R-Studio; case studies of exploratory data analysis and visualization techniques; precision, sensitivity, specificity, over-fitting; decision trees and random forests; clustering methods.

The students learn the implementation and practical application of new (under development) web technologies, particularly in the areas of online media (e.g. web TV, streaming, content protection, social media), telecommunications (e.g. web RTC) , as well as Internet of Things.

We are entering the “Age of Big Data” – an extremely large amount of information is created every day, which is revolutionizing science and technology, governments, economy, and international development. A variety of sources contribute to the Big Data, including the Internet, Wikipedia, social networks (e.g. Facebook), micro blogs, mobile phones, and cameras. This era of “information burst” has brought convenience to our daily lives. More recently, the emergence of foundation models (e.g., GPT) is also an outcome of big data, massive high-quality is the fuel to the success training of these large machine learning models. However, the availability of such a vast amount of information has also created many problems. For example, reported incidents of leakage of private data, due to the use of the Foxy software, and the loss of USB drives that contain thousands of patients’ records, have raised serious legal and social concerns. The goal of this course is to engage students in examining the critical issues that they could encounter in the Age of Big Data. They will examine how Big Data is affecting our society and daily lives and how Big Data is used in our daily life. They will study the security and credibility issues of Big Data. They will also address the issues of organizing and exploring Big Data. Solutions proposed in legal, technological, and education domains will be explored and discussed.

The course gives a thorough understanding of digital integrated circuit design. Increasing complexity and high requirements on performance in the form of throughput and low power consumption increase the expectations from the hardware designer. Understanding both the possibilities and the limitations is important for both full custom designers and high-level designers. The course focuses on CMOS design.

This course focuses on introducing students to the core concepts of the Unix operating system and how to program this system. Today, Unix and Unix-like operating systems are ubiquitous; they are widely used in servers, embedded devices and have a growing desktop and mobile market (Linux, Mac OS X, Android etc.). This course teaches students how to develop applications for such systems, assuming no other software layer but OS. Students improve their existing C programming language skills and learn some key POSIX APIs to support designing and writing programs in a portable, maintainable fashion. They learn how to write multithreaded and multi-process applications as well as some basics of Unix networking. This is done through the Unix command line, and students learn basic tools and how to write shell scripts to automate common tasks. Students need a version of Unix installed on their own laptop (ideally Linux), help with this is provided in the first lab.

This course introduces different techniques of designing and analyzing algorithms. The course covers the framework for algorithm analysis, such as lower bound arguments, average case analysis, and the theory of NP-completeness. In addition, various algorithm design paradigms are studied. The course serves two purposes: to improve ability to design algorithms in different areas, and to prepare for the study of more advanced algorithms. The course covers lower and upper bounds, recurrences, basic algorithm paradigms (such as prune-and-search, dynamic programming, branch-and-bound, graph traversal, and randomized approaches), amortized analysis, NP-completeness, and some selected advanced topics.

This course provides an introduction to medical robotics and its applications. It covers recent developments in robotics for medical applications, position and orientation (POSE) of a robotic system, the kinematics of arm-type and vehicle-type robots, the trajectory of an arm-type robot end effector, and the different biomedical system controls.