ZHANG, Yanlin - HKUST Class Schedule & Quota

Class Schedule & Quota
2025-26 Fall 2024-25 Summer 2024-25 Spring 2021-22 Winter
2025-26 Fall
Common Core Course
The class information on this page will normally be updated every 15 to 20 minutes during class enrollment period.
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This service was first adapted from a student contribution in 2011-12 Fall. The efforts of all concerned are duly acknowledged.

AIAA AMAT BSBE CMAA CNCC CNGF DLED DSAA EOAS FTEC FUNH INFH INTR IOTA IPEN LANG MICS MSSM PDEV PLED ROAS SEEN SMMG SOCH SYSH UCMP UCUG UFUG UGOD

[Matching between Lecture & Lab required]

COURSE INFO

PRE-REQUISITE	UFUG 2601 OR UFUG 2602
DESCRIPTION	This course introduces core data structure and algorithms, which are fundamental to data science and analytics. As an important course that bridges students to a number of advanced courses, it covers topics in asymptotic complexity analysis, typical data structures (stacks, queues, trees, and graphs), sorting, searching, data structure-specific algorithms, algorithmic strategies (e.g., divide-and-conquer, greedy, and dynamic programming), analysis and measurement of programs.

PRE-REQUISITE

UFUG 2601 OR UFUG 2602

DESCRIPTION

This course introduces core data structure and algorithms, which are fundamental to data science and analytics. As an important course that bridges students to a number of advanced courses, it covers topics in asymptotic complexity analysis, typical data structures (stacks, queues, trees, and graphs), sorting, searching, data structure-specific algorithms, algorithmic strategies (e.g., divide-and-conquer, greedy, and dynamic programming), analysis and measurement of programs.

DSAA 2043 - Design and Analysis of Algorithms (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6378)	MoWe 09:00AM - 10:20AM	Rm 101, E1	ZHANG, Yanlin	48	42	6	0
LA01 (6379)	Fr 12:00PM - 12:50PM	Rm 227, E1	ZHANG, Yanlin	48	42	6	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	Genetics and genomics are closely related fields, both analyzing massive biological data. Genetics studies specific genes and their roles in heredity, while genomics encompasses the entire genome of an organism to understand its complex systems. This course aims to introduce traditional computational biology and revolutionary advancements in machine learning that are transforming the fields of genetics and genomics. It begins with a very brief overview of conventional algorithms in genetics and genomics, setting the stage for a deeper exploration of how genetics and genomics have evolved with the advent of machine learning techniques. As the course progresses, students will be introduced to the latest machine learning methods, with an emphasis on deep learning, which has dramatically changed the landscape of genomics research. Through a combination of theoretical instruction and hands-on teamwork projects, this course is designed to equip students with the necessary skills to navigate and contribute to the field of genomics, leveraging machine learning to uncover new insights into genetic data and its implications for human health and disease.

VECTOR

[3-0-0:3]

DESCRIPTION

Genetics and genomics are closely related fields, both analyzing massive biological data. Genetics studies specific genes and their roles in heredity, while genomics encompasses the entire genome of an organism to understand its complex systems. This course aims to introduce traditional computational biology and revolutionary advancements in machine learning that are transforming the fields of genetics and genomics. It begins with a very brief overview of conventional algorithms in genetics and genomics, setting the stage for a deeper exploration of how genetics and genomics have evolved with the advent of machine learning techniques. As the course progresses, students will be introduced to the latest machine learning methods, with an emphasis on deep learning, which has dramatically changed the landscape of genomics research. Through a combination of theoretical instruction and hands-on teamwork projects, this course is designed to equip students with the necessary skills to navigate and contribute to the field of genomics, leveraging machine learning to uncover new insights into genetic data and its implications for human health and disease.

DSAA 6000M - Machine Learning in Genetics and Genomics (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6147)	Fr 09:00AM - 11:50AM	Rm 101, E1	ZHANG, Yanlin	30	15	15	0