VECTOR | [3-0-0:3] |
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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 |
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L01 (6055) | We 09:00AM - 11:50AM | Rm 149, E1 | CHEN, Yingcong | 60 | 56 | 4 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | This is a task-oriented yet interaction-based course, which aims to scrutinize the recent trends and challenges in visual intelligence tasks (from the image restoration to 3D vision tasks). This course will follow the way of flipped-classroom manner where the lecturer teaches the basics; meanwhile, the students will also be focused on active discussions, presentations (lecturing), and hands-on research projects under the guidance of the lecturer in the whole semester. Through this course, students will be equipped with the capability to critically challenge the existing methodologies/techniques and hopefully make breakthroughs in some new research directions. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6056) | Mo 09:00AM - 11:50AM | Rm 150, E1 | WANG, Lin | 30 | 8 | 22 | 0 |
VECTOR | [3-0-0:3] |
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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 |
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L01 (6059) | 22-JAN-2024 - 08-APR-2024 Tu 09:00AM - 11:50AM | Rm 148, E1 | SUN, Ying | 60 | 46 | 14 | 0 | |
09-APR-2024 - 10-MAY-2024 Tu 09:00AM - 11:50AM | Lecture Hall C | SUN, Ying |
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 (6053) | Fr 09:00AM - 11:50AM | Rm 201, E1 | LIANG, Junwei | 30 | 22 | 8 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | This course aims to provide students with key principles and algorithms to build modern autonomous AI systems. Key topics include machine perception, planning and decision-making algorithms. Through this course, students will learn and practice the foundational principles, techniques, and tools to build new autonomous AI systems. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6052) | Fr 01:30PM - 04:20PM | Rm 201, E1 | LIANG, Junwei | 30 | 24 | 6 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | Machine learning is a cornerstone of AI. The course targets beginners who will learn basic and rigorous machine learning methods, including linear regression, logistic regression, decision trees, naïve Bayes, SVM, unsupervised learning, neural networks, graphical models, EM algorithm. The students will be able to enter more advanced machine learning courses after taking this course. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6054) | Mo 09:00AM - 11:50AM | Rm 233, W1 | XIE, Sihong | 40 | 9 | 31 | 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 (6214) | Tu 09:00AM - 11:50AM | Rm 222, W1 | WANG, Hao | 30 | 26 | 4 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | In the era of large-scale deep learning models, multimodal learning based on speech, text, and images is gaining increasing prominence. It holds the potential to facilitate cross-domain applications, improve human-computer interaction, and advance innovation in the field of AI. This course will provide an in-depth exploration of applied deep learning techniques, focusing on their applications in speech processing, natural language understanding, and multimodal data analysis. Students will gain practical experience in building deep learning models for various tasks, including speech recognition, language translation, image analysis, and more. The course covers fundamental concepts, algorithms, and tools in the field of deep learning and emphasizes hands-on projects and real-world applications. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6215) | Tu 09:00AM - 11:50AM | Rm 233, W1 | LIU, Li | 40 | 12 | 28 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | This course provides a comprehensive introduction to the field of quantum computing. Students will explore fundamental concepts such as quantum bits, quantum circuits, and quantum algorithms. Advanced topics including quantum error correction, quantum information processing, and quantum machine learning will also be covered. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6213) | Tu 01:30PM - 04:20PM | Rm 149, E1 | WANG, Xin | 30 | 8 | 22 | 0 |
VECTOR | [1-3 credit(s)] |
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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 |
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R01 (6339) | TBA | TBA | TBA | 20 | 2 | 18 | 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 (6057) | Th 11:00AM - 11:50AM | Rm 101, W1 | LIU, Li | 100 | 33 | 67 | 0 |
VECTOR | [0-1-0:1] |
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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. Continuation of AIAA 6101. Graded P or F. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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T01 (6058) | Fr 11:00AM - 11:50AM | Rm 101, W1 | LIU, Li | 100 | 32 | 68 | 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 (6193) | TBA | TBA | TBA | 100 | 36 | 64 | 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 (6200) | TBA | TBA | TBA | 100 | 4 | 96 | 0 |