Computer Science

Intelligent vehicles can communicate with other vehicles or roadside units and behave autonomously. They are believed to significantly change the way that people move from one place to another. This class introduces fundamental knowledge in intelligent vehicles and then focuses on some specific advanced topics (e.g., security). The knowledge and topics bring state-of-the-art technology to students and develop their skills in system modeling, design, and analysis.

There are mainly four parts in this class:
(1) Background: This part introduces traditional (i.e., without connectivity and autonomy) system architecture, vehicular networks, and basic design and analysis approaches.
(2) Applications: This part introduces applications of intelligent vehicles, including advanced driver-assistance systems, cooperative adaptive cruise control, and intersection management.
(3) Technology: This part introduces the technology which is needed to realize the applications of intelligent vehicles.
(4) Advanced Topics: This part introduces advanced topics such as over-the-air update, security, and certification.

Depending on students' interests, final projects can be survey, implementation, or research. 

This course examines the fundamentals of human-computer interaction to design, develop, and evaluate interactive systems based on information technologies that ensure the accessibility, ergonomics and usability of the systems. Topics include: introduction to human-computer interaction (HCI); interaction models and metaphors; user-centered design; assessment of interactive systems. 

This course offers an introduction to the fundamental principles and concepts of computers and programming with the programming language Python.

Responsible Data Science is examined through the lens of four introductory dimensions: data dimension; algorithm dimension; human dimension, including psychology of human biases and ethics or moral philosophy; design dimension, including data visualization and interaction design and explainable artificial intelligence (XAI).

Throughout this course, students follow lectures and workshops, read literature, engage in class discussions, give presentations, critique, and conduct an investigation on a topic related to a (self-chosen) real-world ethical problem related to data science in a particular domain. The project also contains a practical solution to the problem illustrated in a low-fidelity prototype.

This course discusses topics including database design with ERM/ERDD; theoretical foundations of relational database systems: relational algebra, functional dependencies, and normal forms; relational database development: SQL data definitions, foreign keys and other integrity constraints, and SQL as applicable language: essential language elements, and embedding in programming language; application programming, and object-relational mapping; security and protection concepts; transaction subject, transactional guaranties, synchronization of multi user operations, and fault tolerance features; and application and new developments: data warehousing, data mining, and OLAP. The topics are deepened in an implementation project for student groups.

This course provides an introduction to basic concepts of artificial intelligence. The course addresses heuristic search algorithm and knowledge reasoning of symbolic AI (Artificial Intelligence), a traditional AI approach. In addition, the course addresses theories about computational AI, such as genetic algorithms, and neural network learning, and how they can be applied in each field. The course also studies the application fields of artificial intelligence technologies.