RIKOS, APOSTOLOS - HKUST Class Schedule & Quota

Class Schedule & Quota
2024-25 Spring 2024-25 Fall 2023-24 Summer 2021-22 Winter
2024-25 Spring
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 DSAA EOAS FTEC FUNH INFH INTR IOTA IPEN LANG MICS 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 (6249)	Fr 09:00AM - 09:50AM	Rm 102, E4	CHEN, Jintai CHEN, Yingcong DAI, Enyan HU, Xuming LIANG, Junwei LIU, Li RIKOS, APOSTOLOS SUN, Ying WANG, Hao WANG, Xin XIE, Sihong XIE, Zeke YUE, Yutao	100	0	100	0

COURSE INFO

VECTOR	[3-0-0:3]
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.

VECTOR

[3-0-0:3]

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 (6005)	Fr 01:30PM - 04:20PM	Rm 150, E1	RIKOS, APOSTOLOS	40	0	40	0