RIKOS, APOSTOLOS - HKUST Class Schedule & Quota

Class Schedule & Quota
2024-25 Summer 2024-25 Spring 2024-25 Fall 2021-22 Winter
2024-25 Summer
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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AIAA AMAT BSBE CMAA DSAA FUNH INFH INTR IOTA ROAS SMMG SOCH SYSH UCUG UFUG

COURSE INFO

PRE-REQUISITE	UFUG 1103 OR UFUG 1106
DESCRIPTION	This course aims to teach students the basic math concepts for Artificial Intelligence (AI). Key topics include fundamental Linear Algebra (Matrix Calculations, Norms, Eigenvectors and Eigenvalues), Calculus (Derivative, Taylor series, Multivariate Calculus), and Probability Theory (Distributions, Statistics of Random Variables, Bayes’ theorem). With these mathematical concepts, some basic principles of numerical optimization and typical AI algorithms (Gradient Descent, Maximum-likelihood, Regression, Least Square Estimation, Spectral Clustering, Matrix Decomposition, etc.) will also be introduced as examples to better relate math to AI. The approach of this course is specifically AI application oriented, aiming to help students to quickly establish a fundamental mathematical knowledge structure for AI studies. Through this course, students will acquire the fundamental mathematical concepts required for AI, understand the connections between AI and mathematics, and get prepared to learn the mathematical principles, formulas, inductions, and relevant proofs for advanced AI algorithms.

PRE-REQUISITE

UFUG 1103 OR UFUG 1106

DESCRIPTION

This course aims to teach students the basic math concepts for Artificial Intelligence (AI). Key topics include fundamental Linear Algebra (Matrix Calculations, Norms, Eigenvectors and Eigenvalues), Calculus (Derivative, Taylor series, Multivariate Calculus), and Probability Theory (Distributions, Statistics of Random Variables, Bayes’ theorem). With these mathematical concepts, some basic principles of numerical optimization and typical AI algorithms (Gradient Descent, Maximum-likelihood, Regression, Least Square Estimation, Spectral Clustering, Matrix Decomposition, etc.) will also be introduced as examples to better relate math to AI. The approach of this course is specifically AI application oriented, aiming to help students to quickly establish a fundamental mathematical knowledge structure for AI studies. Through this course, students will acquire the fundamental mathematical concepts required for AI, understand the connections between AI and mathematics, and get prepared to learn the mathematical principles, formulas, inductions, and relevant proofs for advanced AI algorithms.

AIAA 2711 - Mathematics for AI (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6043)	16-JUN-2025 - 09-JUL-2025 MoWeTh 06:00PM - 09:20PM	Rm 102, W1	RIKOS, APOSTOLOS	40	0	40	0	> Add/Drop Deadline: 18 June 2025