COURSE INFO
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6417) Mo 04:30PM - 05:20PMRm 101, W1BAI, Ge
CHEN, Huangxun
CHU, Xiaowen
KAN, Ge Lin
LIANG, Junwei
QIN, Chengwei
RIKOS, APOSTOLOS
WANG, Xin
WANG, Zeyu
XIE, Sihong
XIE, Zeke
YANG, Menglin
YUE, Yutao		10001000 
COURSE INFO
PRE-REQUISITEUFUG 1103 OR UFUG 1106
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L02 (6419) Mo 09:00AM - 11:50AMRm 147, E1RIKOS, APOSTOLOS60
Quota/Enrol/Avail
Year 2 UG students: 30/0/30
Year 3 UG Students: 30/0/30
0600 
COURSE INFO
PRE-REQUISITEUFUG 2104 AND (UFUG 2601 OR UFUG 2602)
DESCRIPTIONThe implementation of autonomous systems requires agents to learn how to make decisions. Reinforcement learning is a powerful paradigm for achieving such a goal, and it is relevant to an enormous range of tasks, including robotics, game playing, operations research, healthcare and more. This course provides a solid introduction to the field of reinforcement learning. Students learn about the core challenges and approaches, including generalization and exploration. Through the combination of lectures, written and coding assignments, and course projects, students are equipped with modeling and learning algorithm techniques for sequential decision-making problems. Assignments include the basics of reinforcement learning as well as deep reinforcement learning — an extremely promising new area that combines deep learning advancements with reinforcement learning.

AIAA 3053 - Reinforcement Learning: Principles and Methods (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6420) Fr 12:00PM - 02:50PMRm 148, E1RIKOS, APOSTOLOS50
Quota/Enrol/Avail
BEng (AI) Year 3 Students: 50/0/50
0500