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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 (6277) | Fr 03:00PM - 03:50PM | Rm 101, W1 | CHEN, Lei CHEN, Yingcong DAI, Enyan HU, Xuming LIANG, Junwei LIU, Li RIKOS, APOSTOLOS SUN, Ying WANG, Hao WANG, Xin XIE, Sihong XIONG, Hui YUE, Yutao ZHONG, Bingzhuo | 136 | 85 | 51 | 0 |
| 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 (6278) | MoWe 09:00AM - 10:20AM | Rm 102, E4 | CHEN, Yingcong LIANG, Junwei XIONG, Hui | 100 | 96 | 4 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course helps students to get basic knowledge about deep neural networks, helping them to understand basic concepts, capabilities and challenges of deep neural networks. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6092) | Tu 09:00AM - 11:50AM | Rm 102, W4 | CHEN, Yingcong | 60 | 58 | 2 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course covers how to program with Python and use it to solve practical problems in Artificial Intelligence. Topics include basic Python usage (e.g., syntax, data structure, etc.) and important packages for data analysis and machine learning applications (e.g., NumPy, SciPy, etc.). The students will be guided to practice on simple artificial intelligence tasks. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6094) | Mo 09:00AM - 11:50AM | Rm 149, E1 | SUN, Ying | 60 | 56 | 4 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course covers typical algorithms and data structures. Topics include core methodologies of algorithm design, standard data structures, and typical algorithms and data structures that have been widely adopted for solving different problems, covering from fundamental ones (e.g., searching and sorting algorithms) to more advanced ones (e.g., graph algorithms, number theory algorithms, FFT). |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6095) | Tu 09:00AM - 11:50AM | Rm 147, E1 | SUN, Ying | 30 | 24 | 6 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course will explore the basic knowledge and the latest advances of Deep Learning based methods in the medical field, with special attention to challenges and opportunities for Medical AI. This course will provide students the opportunity to learn skills to train/learn/develop Deep Learning models from medical data with several case studies. It covers knowledge on Digital Medical Imaging, supervised learning, semi-supervised learning, and unsupervised learning related with Medical AI research. Importantly, some hot topics on "AI for Medical" will be introduced. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6096) | Fr 09:00AM - 11:50AM | Rm 222, W1 | LIU, Li | 40 | 38 | 2 | 0 |
| 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 (6241) | Fr 09:00AM - 11:50AM | Rm 102, E1 | XIE, Sihong | 30 | 11 | 19 | 0 |
| 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 (6240) | Tu 01:30PM - 04:20PM | Rm 223, W1 | YUE, Yutao | 30 | 24 | 6 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | In the era of big data and artificial intelligence, information theory has become an indispensable tool for machine learning practitioners. This course aims to bridge the gap between classical information theory and its cutting-edge applications in machine learning. Students will explore the foundations of information measures, data compression, hypothesis testing, channel coding, channel capacity, entropies, and divergences, as well as their statistical learning applications. Through guest lectures by leading experts, we will also delve into the frontiers of information theory in machine learning. By the end of this course, students will be equipped with the knowledge and skills necessary to apply information theory to develop more efficient and effective machine learning technologies. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6093) | Tu 01:30PM - 04:20PM | Rm 201, E1 | WANG, Xin | 30 | 27 | 3 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course offers an in-depth exploration of Natural Language Processing (NLP), emphasizing transformative neural network architectures like RNNs and transformers. Students will engage with core NLP tasks such as language modeling and machine translation, and examine the impacts of recent innovations like Large Language Models. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6099) | Mo 01:30PM - 04:20PM | Rm 149, E1 | HU, Xuming | 30 | 30 | 0 | 0 |
| VECTOR | [1-3 credit(s)] |
|---|---|
| DESCRIPTION | An independent research project carried out under the supervision of a faculty member. Graded P or F. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6442) | TBA | TBA | TBA | 5 | 5 | 0 | 0 |
| VECTOR | [0-1-0:1] |
|---|---|
| DESCRIPTION | Series of seminars presenting research problems currently under investigation, presented by faculty, students, and visiting speakers. Students are expected to attend regularly. Continuation of AIAA 6101. Graded P or F. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| T01 (6470) | We 11:00AM - 11:50AM | Rm 101, E1 | WANG, Hao | 80 | 80 | 0 | 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 (6100) | TBA | TBA | TBA | 999 | 48 | 951 | 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 (6101) | TBA | TBA | TBA | 999 | 12 | 987 | 0 |