RIKOS, APOSTOLOS - HKUST Class Schedule & Quota

Class Schedule & Quota
2026-27 Fall 2025-26 Summer 2025-26 Spring 2025-26 Winter
2026-27 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

COURSE INFO

DESCRIPTION	This course provides guidance to undergraduate students of the AI major for their academic path and future. This course is mostly introductory and aims to inspire UG students for their academic path development and growth of maturity during their UG study. Activities may include seminars, workshops, advising and sharing sessions, interaction with faculty and teaching staff, and discussion with student peers or alumni. Graded P or F.

DESCRIPTION

This course provides guidance to undergraduate students of the AI major for their academic path and future. This course is mostly introductory and aims to inspire UG students for their academic path development and growth of maturity during their UG study. Activities may include seminars, workshops, advising and sharing sessions, interaction with faculty and teaching staff, and discussion with student peers or alumni. Graded P or F.

AIAA 1010 - Academic Orientation for AI Students (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6541)	Fr 12:00PM - 12:50PM	Lecture Hall B	BAI, Ge CHEN, Changhao CHEN, Yingcong CHU, Xiaowen DAI, Enyan HU, Zhiming LIU, Li RIKOS, APOSTOLOS SHU, Yao XIE, Zeke YANG, Menglin YUE, Yutao ZHONG, Bingzhuo	200	0	200	0

COURSE INFO

DESCRIPTION	Learning to make good decisions is one of the keys to autonomous systems. This course will focus on Reinforcement Learning (RL), a currently very active subfield of artificial intelligence, and it will discuss selectively a number of algorithmic topics including Markov Decision Process, Q-Learning, function approximation, exploration and exploitation, policy search, imitation learning, model-based RL and optimal control. This course provides both the foundations and techniques for developing RL and deep RL algorithms that interact with physical environments, and real application cases of RL will be introduced. Basic knowledge of machine learning and mathematical optimization are expected for this course.

DESCRIPTION

Learning to make good decisions is one of the keys to autonomous systems. This course will focus on Reinforcement Learning (RL), a currently very active subfield of artificial intelligence, and it will discuss selectively a number of algorithmic topics including Markov Decision Process, Q-Learning, function approximation, exploration and exploitation, policy search, imitation learning, model-based RL and optimal control. This course provides both the foundations and techniques for developing RL and deep RL algorithms that interact with physical environments, and real application cases of RL will be introduced. Basic knowledge of machine learning and mathematical optimization are expected for this course.

AIAA 5034 - Reinforcement Learning and Optimization (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6026)	Th 01:30PM - 04:20PM	Rm 228, E2	RIKOS, APOSTOLOS	80	0	80	0

[Matching between Lecture & Tutorial required]

COURSE INFO

PRE-REQUISITE	UFUG1103 or UFUG 1106
DESCRIPTION	This course emphasizes a thorough study of vectors, vector functions, partial derivatives, multiple integrals, and vector calculus. Students should understand the basic concepts and techniques of vector calculus and be proficient at vector operations and multivariable calculus calculation. After taking this course, students will understand the following implementations of functions of several variables: limits, continuity, derivatives, and integration, and will be able to apply multivariable calculus in solving practical problems in the real world.

PRE-REQUISITE

UFUG1103 or UFUG 1106

DESCRIPTION

This course emphasizes a thorough study of vectors, vector functions, partial derivatives, multiple integrals, and vector calculus. Students should understand the basic concepts and techniques of vector calculus and be proficient at vector operations and multivariable calculus calculation. After taking this course, students will understand the following implementations of functions of several variables: limits, continuity, derivatives, and integration, and will be able to apply multivariable calculus in solving practical problems in the real world.

UFUG 2101 - Introduction to Multivariable Calculus (3 units)

Section	Date & Time	Room	Instructor	Quota	Avail
L02 (6789)	Mo 03:00PM - 04:20PM	Rm 235, E1	RIKOS, APOSTOLOS	35	35
L02 (6789)	Fr 10:30AM - 11:50AM	Rm 235, E1	RIKOS, APOSTOLOS	35	35
T02 (6792)	Fr 09:30AM - 10:20AM	Rm 235, E1	RIKOS, APOSTOLOS	35	35