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. |
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Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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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 |
PRE-REQUISITE | UFUG 2601 OR UFUG 2602 |
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DESCRIPTION | The objective of this course is to present an overview of the principles and practices of AI and to address complex real-world problems. Through introduction of AI tools and techniques, the course helps students develop a basic understanding of problem solving, search, theorem proving, knowledge representation, reasoning and planning methods of AI; and develop practical applications in vision, language, and so on. Topics include foundations (search, knowledge representation, machine learning and natural language understanding) and applications (data mining, decision support systems, adaptive web sites, web log analysis). |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6245) | TuFr 01:30PM - 02:50PM | Rm 102, E4 | LIU, Li YUE, Yutao | 100 | 0 | 100 | 0 |
DESCRIPTION | This introductory course surveys the explosive area of AI ethics and illuminates relevant AI concepts with no prior background needed. Key topics include Fake News Bots; AI Driven Social Media Displacing Traditional Journalism; drone Warfare; Elimination of Traditional Jobs; Privacy-violating Advertising; Monopolistic Network Effects; Biased AI Decision/Recognition Algorithms; Deepfakes; Autonomous Vehicles; Automated Hedge Fund Trading, etc. Through the course, students will be able to understand how human civilization will survive amid the rise of AI, what are the new rules in the new era, how to preserve ethics when facing the threats of extinction and what are engineers’ and entrepreneurs’ ethical responsibilities. |
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Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6250) | MoWe 07:30PM - 08:50PM | Rm 202, E3 | HU, Xuming | 30 | 0 | 30 | 0 |
PRE-REQUISITE | UFUG 1103 OR UFUG 1106 |
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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. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6247) | MoWe 06:00PM - 07:20PM | Rm 102, E4 | ZHONG, Bingzhuo | 100 | 0 | 100 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | This course aims to provide students with an overview of Artificial Intelligence (AI) principles and techniques. Key topics include machine learning, search, game theories, Markov decision process, constraint satisfaction problems, Bayesian networks, etc. Through this course, students will learn and practice the foundational principles, techniques and tools to tackle new AI problems. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6001) | Th 09:00AM - 11:50AM | Rm 202, E3 | LIANG, Junwei | 40 | 0 | 40 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | This course introduces potential security and privacy vulnerabilities in Artificial Intelligence (AI) and covers basic and advanced protections. Topics include security and privacy risks in AI technologies, the goal of C.I.A. (Confidentiality, Integrity and Availability) in AI technologies, basic and advanced cryptography, protocol designs for AI security and privacy, etc. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6002) | Fr 09:00AM - 11:50AM | Rm 228, E2 | ZHONG, Bingzhuo | 50 | 0 | 50 | 0 |
VECTOR | [3-0-0:3] |
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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. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6005) | Fr 01:30PM - 04:20PM | Rm 150, E1 | RIKOS, APOSTOLOS | 40 | 0 | 40 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | This course focuses on the Artificial Intelligence (AI) techniques and applications in multimodal tasks, which involve processing, fusing, and generating contents from multiple data modalities, such as images, videos, text etc. The course will cover the challenges, state-of-the-art methods, as well as hands-on experience in implementing and evaluating multi-modal deep learning models. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6004) | We 09:00AM - 11:50AM | Rm 223, W1 | WANG, Hao | 30 | 0 | 30 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | Graph mining methods have been investigated for various applications including financial analysis, traffica prediction, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that existing graph mining methods can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data, and lack interpretability. In this course, representative graph mining models and their inner mechanisms will be discussed. Then, we will introduce the trustworthy graph mining methods in privacy, robustness, fairness, and explainability. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6010) | TuTh 10:30AM - 11:50AM | Rm 233, W1 | DAI, Enyan | 30 | 0 | 30 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | This course delves into essential machine learning and deep learning algorithms for healthcare applications, covering areas such as life sciences discovery, medical image analysis, biosignal processing, medical data mining, and foundational models for electronic health records. Core topics include key methodologies for algorithm design, commonly used data structures, and widely adopted algorithms tailored to diverse healthcare challenges. Additionally, the course highlights the latest advancements in this rapidly evolving field. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6011) | Mo 01:30PM - 04:20PM | Rm 102, E1 | CHEN, Jintai | 30 | 0 | 30 | 0 |
VECTOR | [0 credit] |
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DESCRIPTION | Series of seminars presenting research problems currently under investigation, presented by faculty, students, and visiting speakers. Students are expected to attend regularly. Graded P or F. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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T01 (6006) | We 11:00AM - 11:50AM | Rm 102, E4 | CHEN, Jintai | 80 | 0 | 80 | 0 |
DESCRIPTION | Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned. |
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Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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R01 (6007) | TBA | No room required | TBA | 999 | 45 | 954 | 0 |
DESCRIPTION | Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned. |
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Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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R01 (6008) | TBA | No room required | TBA | 999 | 12 | 987 | 0 |