This course covers a wide range of the concepts, history, examples, and applications of artificial intelligence, cultivating the ability to understand the past and present of artificial intelligence technology, which has recently developed rapidly, and predict how future society will change in the future.
This course covers theoretical backgrounds and practical implementation of different machine learning techniques, including supervised learning, unsupervised learning, deep learning, active learning, anmd reinforcement learning. In addition, it explores the entire pipeline to build applications of machine learning with practices. Furthermore, it provides a broad introduction to ethical issues relevant to machine learning.
This course aims to provide broad knowledge about interaction design methods and principles for better usability through hands-on experiences in user-centered design sessions. The course covers well-known design principles on usability aspects (e.g., learnability, efficiency, human errors) and design methodologies (e.g., user-centered design, task analysis, prototyping, heuristic evaluation, and user testing). Design assignments and term projects will help students enhance their user interface design skills in web, mobile, and IoT environments.
This course introduces fundamental concepts and theories to design and analyze computer algorithms that are widely employed in computer science. Throughout the course, we will cover divide-and-conquer, dynamic programming, greedy algorithms, backtracking, branch-and-bound, genetic algorithms, and NP theory.
This course covers fundamental concepts of database and database management systems and how to build and query your own database using Structured Query Language. In addition, you will learn a theoretical background for designing a **good** database and basic techniques for concurrency control and recovery.
This course aims to cultivate practical skills to solve programming challenges based on what students learned from the data structure and additional techniques, including string manipulation, sorting, backtracking, graph traversal, and dynamic programming.
This course covers a wide range of the concepts, history, examples, and applications of artificial intelligence, cultivating the ability to understand the past and present of artificial intelligence technology, which has recently developed rapidly, and predict how future society will change in the future.