LIANG, Yuxuan - HKUST Class Schedule & Quota

Class Schedule & Quota
2024-25 Fall 2023-24 Summer 2023-24 Spring 2021-22 Winter
2024-25 Fall
Common Core Course
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COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	The 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.

VECTOR

[3-0-0:3]

DESCRIPTION

The 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)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6158)	02-SEP-2024 - 06-DEC-2024 Mo 06:00PM - 08:50PM	Rm 202, E3	LIANG, Yuxuan	30	13	17	0