COURSE INFO
VECTOR[3-0-0:3]
EXCLUSIONIOTA 5003
CO-LIST WITHIOTA 5003
DESCRIPTIONThis course aims to develop students’ fundamental understanding of the application scenarios, challenges, and solutions of wireless connectivity in various systems involving autonomous things, and under possible mobility. Topics covered include fundamentals of digital communications, future wireless connectivity requirements, and various solutions to the unique challenges such as dynamic propagation environment, scalability, complexity, and heterogeneity.

INTR 5220 - Wireless Connectivity for Mobile Autonomous Things (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6140) Tu 09:00AM - 11:50AMRm 202, W4YAN, Jia
YANG, Liuqing		153120 
COURSE INFO
VECTOR[3-0-0:3]
PREVIOUS CODEINTR 6000F
DESCRIPTIONThe course aims to help students master the basic concepts and research methods of Artificial Intelligence (AI) and machine learning, understand future development trends, and lay the foundation for further research in leveraging machine learning and AI in transportation research. Through the study of this course, students will understand and master the basic concepts, ideas and methods of AI and related machine learning techniques, and initially learn and master the ability to use those machine learning techniques to solve practical problems, especially in transportation context.

INTR 5250 - Artificial Intelligence in Transportation (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6142) Mo 09:00AM - 11:50AMRm 233, W1ZHENG, Xinhu302640 
COURSE INFO
VECTOR[3-0-0:3]
PREVIOUS CODEINTR 6000D
DESCRIPTIONThis course will explore the fundamental theories and methodologies of linear optimization and demonstrate how these techniques can be used to solve practical optimization problems. Two typical linear optimization techniques, linear programming and integer programming, will be introduced and discussed. The first part, linear programming, explores the simplex algorithm and the duality theory that act as the cornerstones of modern linear optimization solvers. The second part, integer programming, covers a broader range of topics in both methodology and applications, including problem modeling, model analysis, and decomposition- and relaxation-based solution methods. Implementation issues and industry cases will also be discussed.

INTR 5310 - Theory and Application of Linear Optimization (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6145) We 01:30PM - 04:20PMRm 101, E4JIA, Shuai3010200 
COURSE INFO
VECTOR[3-0-0:3]
EXCLUSIONIOTA 5108
CO-LIST WITHIOTA 5108
DESCRIPTIONThis course aims to develop students’ fundamental understanding of the theory and application of incremental learning and adaptive signal processing. Topics covered in this course include Wiener filter, least mean squares (LMS), recursive least squares (RLS), the Kalman filter, classification, parameter learning, neural network and deep learning.

INTR 5320 - Incremental Learning and Adaptive Signal Processing (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6146) Th 09:00AM - 11:50AMRm 228, E2GONG, Zijun
YANG, Liuqing		206140 
[DELI]
Mode of Delivery
COURSE INFO
ATTRIBUTES[BLD] Blended learning
VECTOR[3-0-0:3]
EXCLUSIONROAS 5900
CO-LIST WITHROAS 5900
PREVIOUS CODEINTR 5600
DESCRIPTIONThe course will cover a wide range of analytical methods used in human factors research domain. The students will gain an understanding of the procedures, objectives and limitations of different research methods. The course will also include four case studies so that students would gain first-hand experience in applying the methods in real projects. These contents are required for research investigating users’ behaviors.

INTR 5330 - Analytical Methods in Human Factors Research (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6148) Fr 09:00AM - 11:50AMRm 202, W4HE, Dengbo154110 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course introduces students to various modeling and simulation methods in transportation-energy systems. Methods covered include energy demand and supply modeling, integrated transport-energy system modeling with machine learning and optimization, and system simulation. Practical applications of these methods are illustrated through state-of-art case studies and research papers.

INTR 6000L - Modeling and Simulation in Transportation-Energy Systems (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6155) TBATBATBA300300 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThe postgraduate course delves into the fusion of deep learning techniques with human mobility analytics. In an era dominated by extensive data availability, the course addresses the complexities of mobility analytics using cutting-edge deep learning methodologies. Students will gain a foundational understanding of deep learning concepts and apply them to decipher intricate large-scale human mobility data. The course aims to empower students to contribute meaningfully to fields such as urban planning, transportation management, and public health. Through literature review and hands-on projects, students will develop proficiency in applying deep learning to real-world mobility challenges, fostering a well-rounded skill set for addressing the evolving landscape of human mobility analytics.

INTR 6000M - Deep Learning for Human Mobility Analytics (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6156) Mo 03:00PM - 05:50PMRm 105, W3LIANG, Yuxuan3014160 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONNavigation is a core capability for intelligent vehicles, enabling environment perception, localization, and decision-making. This course provides a comprehensive understanding of vision-based navigation for unmanned systems, focusing on mobile robots and self-driving vehicles. It covers a wide range of topics, including multiview geometry, visual/-inertial state estimation, simultaneous localization and mapping (SLAM), place recognition, scene perception, and recent advances in embodied AI, equipping students with the theoretical and practical skills to design advanced navigation systems. Through hands-on projects using real-world datasets, students will gain experience in implementing and evaluating visual navigation solutions. Ideal for those interested in intelligent systems, computer vision, and AI, this course bridges theory and practice for autonomous systems development.

INTR 6000P - Visual Navigation for Intelligent Vehicles (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6159) Tu 01:30PM - 04:20PMMaker Space W1-621KCHEN, Changhao3017130 
COURSE INFO
VECTOR[0-1-0:0]
DESCRIPTIONSeminar topics presented by students, faculty and guest speakers. Students are expected to attend regularly and demonstrate proficiency in presentation in accordance with the program requirements. Graded P or F.

INTR 6800J - Seminar in Intelligent Transportation (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
T01 (6163) Mo 01:30PM - 02:20PMRm 101, E1YAN, Jia8050300 
COURSE INFO
DESCRIPTIONMaster's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

INTR 6990 - MPhil Thesis Research (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6046) TBANo room requiredTBA8010700 
COURSE INFO
DESCRIPTIONOriginal and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

INTR 7990 - Doctoral Thesis Research (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6047) TBANo room requiredTBA12061590