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| 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 |
|---|---|---|---|---|---|---|---|---|
| L01 (6417) | Mo 04:30PM - 05:20PM | Rm 101, W1 | BAI, 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 | 100 | 55 | 45 | 0 | The class will be delivered by the following instructors and contents as below. W1: Zeke XIE - Overview of Deep Learning Theory W2: Ge Lin Kan - Generative AI for Urban Digital Twin W3: Menglin YANG - Foundation of LLM: From Pretraining, Mid Training to Post Training W4: Xiaowen CHU - Magic of Low-Precision Computing behind LLMs W5: Sihong XIE - Interpretable AI W6: Xin WANG - Foundations of Quantum Computing W7: Ge BAI - Quantum Entanglement and Applications W8: Chengwei Qin - LLM Reasoning and Agent W9: Zeyu WANG - Toward Synergistic Human-AI Content Creation W10: Huangxun CHEN - Overview of Embedded AI W11: Yutao YUE - Towards Conscious AI W12: Junwei LIANG - Towards General Service Embodied AI W13: Apostolos RIKOS - AI and Multi-Agent Networks |
| PRE-REQUISITE | UFUG 2601 OR UFUG 2602 |
|---|---|
| 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 |
|---|---|---|---|---|---|---|---|---|
| L01 (6400) | Mo 09:00AM - 11:50AM | Rm 148, E1 | LIU, Li | 50 Quota/Enrol/Avail For UG year 2 & above students: 50/38/12 | 38 | 12 | 6 | |
| L02 (6401) | We 01:30PM - 04:20PM | Rm 147, E1 | CHEN, Jintai | 50 Quota/Enrol/Avail For UG year 2 & above students: 50/32/18 | 32 | 18 | 5 |
| 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6418) | Mo 01:30PM - 02:50PM | Rm 101, E1 | ZHONG, Bingzhuo | 90 Quota/Enrol/Avail Year 2 UG students: 85/83/2 Year 3 UG Students: 5/2/3 | 85 | 5 | 14 | |
| Fr 09:00AM - 10:20AM | Rm 101, E1 | ZHONG, Bingzhuo | ||||||
| L02 (6419) | Mo 09:00AM - 11:50AM | Lecture Hall C | RIKOS, APOSTOLOS | 90 Quota/Enrol/Avail Year 2 UG students: 85/53/32 Year 3 UG Students: 5/1/4 | 54 | 36 | 8 |
| PRE-REQUISITE | UFUG 2104 AND (UFUG 2601 OR UFUG 2602) |
|---|---|
| DESCRIPTION | The 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6420) | Fr 12:00PM - 02:50PM | Rm 101, E1 | RIKOS, APOSTOLOS | 80 | 80 | 0 | 7 |
| PRE-REQUISITE | Prerequisites: UFUG 2601 OR UFUG 2602 |
|---|---|
| DESCRIPTION | Computer vision has attracted a lot of attention in the recent years. Its application scope covers autonomous driving, security surveillance, intelligent industrialization, remote health monitoring, etc. This course will start by providing students with some essential background knowledge, then drive students to dive into the field by participating a mini project that is derived from real world practical needs. Students are encouraged to acquire advanced knowledge through self-learning to accomplish the mini project. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6424) | Tu 01:30PM - 03:20PM | Rm 147, E1 | CHEN, Yingcong | 48 | 48 | 0 | 22 | |
| L02 (6425) | Th 01:30PM - 03:20PM | Rm 101, E4 | WANG, Hao | 48 | 48 | 0 | 19 | |
| LA01 (6426) | Tu 03:30PM - 04:20PM | Rm 228, E1 | CHEN, Yingcong | 48 | 48 | 0 | 22 | |
| LA02 (6427) | Th 03:30PM - 04:20PM | Rm 227, E1 | WANG, Hao | 48 | 48 | 0 | 19 |
| PRE-REQUISITE | UFUG 2601 OR UFUG 2602 |
|---|---|
| EXCLUSION | DSAA 3051 |
| CROSS CAMPUS COURSE EQUIVALENCE | COMP 4221 |
| DESCRIPTION | This course aims to provide an introduction to the basic elements of natural language processing. This course introduces a variety of ways to represent human languages (like English and Chinese) as computational systems, exploiting these representations for writing programs that do neat stuff with text and speech data including translation, summarization, question answering, etc. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, politics and so on. This course covers computational treatments of words, sounds, sentences, meanings and conversations. It also introduces the state-of-the-art approaches to applications like translation and information extraction. Through lectures, assignments and a final project, students will achieve necessary skills to understand, design and implement their own neural network models. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6428) | MoWe 12:00PM - 01:20PM | Rm 101, E1 | XIE, Sihong | 80 | 80 | 0 | 9 |
| PRE-REQUISITE | UFUG 2104 OR AIAA 2711 |
|---|---|
| CROSS CAMPUS COURSE EQUIVALENCE | COMP 4641 |
| DESCRIPTION | This course is an introduction to social information network analysis and engineering. Students will learn both mathematical and programming knowledge for analyzing the structures and dynamics of typical social information networks (e.g., Facebook, Twitter, and MSN). They will also learn how social metrics can be used to improve computer system design as people are the networks. It will cover topics such as small world phenomenon; contagion, tipping and influence in networks; models of network formation and evolution; the web graph and PageRank; social graphs and community detection; measuring centrality; greedy routing and navigations in networks; introduction to game theory and strategic behavior; social engineering; and principles of computer system design. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6429) | Mo 06:00PM - 07:20PM | Rm 147, E1 | DAI, Enyan | 60 | 60 | 0 | 4 | |
| LA01 (6430) | Tu 12:00PM - 01:20PM | Rm 227, E1 | DAI, Enyan | 60 | 60 | 0 | 4 | |
| Tu 12:00PM - 01:20PM | Rm 228, E1 | DAI, Enyan |
| PRE-REQUISITE | DSAA 2043 |
|---|---|
| DESCRIPTION | The course aims to introduce advanced methods for designing and analyzing algorithms for solving tough problems. The topics cover useful advanced data structures, graph algorithms, heuristic searching algorithms and optimization algorithms that promote the development of AI. Also, students will learn algorithm design and analysis skills for computationally intractable problems, such as NP-completeness, randomized algorithms, approximation algorithms and amortized analysis. Additional topics, such as string matching, geometric and number-theoretic algorithms, will also be introduced. The course presents the topics at an introductory level and aims at senior undergraduate students. Through the class, students will develop a deeper understanding of algorithm design and demonstrate a basic understanding of the principles of advanced algorithms. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6431) | Fr 09:00AM - 11:50AM | Rm 201, W2 | BAI, Ge | 40 | 35 | 5 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course covers popular topics in computer vision, which includes high-level tasks like image classification, object detection, image segmentation, and low-level tasks like image generation, image enhancement, image-to-image translation, etc. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6033) | Th 01:30PM - 04:20PM | Rm 134, E1 | CHEN, Yingcong | 60 Quota/Enrol/Avail PhD (AI): 40/40/0 | 60 | 0 | 75 |
| VECTOR | [3-0-0:3] |
|---|---|
| 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 |
|---|---|---|---|---|---|---|---|---|
| L01 (6034) | Mo 01:30PM - 04:20PM | Rm 101, W1 | LIANG, Junwei | 120 | 115 | 5 | 4 |
| VECTOR | [3-0-0:3] |
|---|---|
| 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 |
|---|---|---|---|---|---|---|---|---|
| L01 (6035) | Mo 09:00AM - 11:50AM | Rm 102, W4 | ZHONG, Bingzhuo | 50 Quota/Enrol/Avail PhD (AI): 30/24/6 | 44 | 6 | 33 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Artificial Intelligence technologies have been maturing and are deployed in real-world applications, such as healthcare, entertainment, business, scientific research, military, etc. In all these domains, the decisions made by AI algorithms can critically impact individuals, organizations and society. The designers, auditors, and users of AI technologies thus need to be equipped with the capabilities to understand, analyze, and eventually discipline these algorithms in the broader contexts. This course will introduce students to the latest research of responsible AI and explore these capabilities in both theoretical and practical ways. Topics include but are not limited to theories and algorithms of secure machine learning, fair machine learning, interpretable AI, and case studies involving natural language processing, computer vision, and reinforcement learning. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6036) | Fr 09:00AM - 11:50AM | Rm 150, E1 | XIE, Zeke | 40 Quota/Enrol/Avail PhD (AI): 40/24/16 | 24 | 16 | 15 |
| VECTOR | [3-0-0:3] |
|---|---|
| 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 |
|---|---|---|---|---|---|---|---|---|
| L01 (6038) | Tu 01:30PM - 04:20PM | Rm 101, W4 | WANG, Hao | 45 | 45 | 0 | 77 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | The study of consciousness is referred to as the "ultimate challenge of artificial intelligence." This course provides instruction and discussions in the field of machine consciousness. The main content includes an introduction to consciousness research, mainstream theories of consciousness, research on self-awareness, attention mechanisms, optimization of intelligent agent goals, subjectivity and affective computing, consciousness modeling and evaluation of artificial intelligence systems, and analysis and control of risks related to machine consciousness. Through this course, participants can gain a fairly comprehensive and in-depth understanding of the research history and current status of the field of machine consciousness, and engage in collaborative research on several specific issues. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6040) | Fr 03:00PM - 05:50PM | Rm 202, E3 | YUE, Yutao | 40 Quota/Enrol/Avail PhD (AI): 20/20/0 | 40 | 0 | 22 |
| VECTOR | [0 credit] |
|---|---|
| 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 |
|---|---|---|---|---|---|---|---|---|
| T01 (6366) | We 01:30PM - 02:20PM | Lecture Hall A | ZHONG, Bingzhuo | 180 | 176 | 4 | 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 |
|---|---|---|---|---|---|---|---|---|
| R01 (6003) | TBA | No room required | TBA | 999 | 97 | 902 | 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 |
|---|---|---|---|---|---|---|---|---|
| R01 (6004) | TBA | No room required | TBA | 999 | 40 | 959 | 0 |