Postgraduate Courses
AIAA
Artificial Intelligence
- AIAA 5023Foundations of Deep Neural Networks[3-0-0:3]DescriptionThis course helps students to get basic knowledge about deep neural networks, helping them to understand basic concepts, capabilities and challenges of deep neural networks.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate a general comprehension of fundamental knowledge in deep neural networks.
- 2.Demonstrate comprehension of modern research topics in deep neural networks.
- 3.Demonstrate comprehension of the application of deep neural networks.
- 4.Recognize the application scope of current methods in deep neural networks.
- 5.Demonstrate oral/writing skills in computer vision community.
- AIAA 5024Advanced Deep Learning[3-0-0:3]Prerequisite(s)COMP 5212DescriptionThis course covers recent developments in deep learning. Topics include meta learning, model compression, federated learning, representation learning, explainable AI, adversarial attack and defense, and advances in deep learning theory.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate a comprehension of advanced knowledge of deep learning.
- 2.Demonstrate a comprehension of Modern research topics in deep learning.
- 3.Demonstrate a comprehension of applications of deep learning methods.
- 4.Recognize the limitations of current methods and make improvements.
- 5.Apply programming and data analytic skills.
- AIAA 5026Computer Vision and Its Applications[3-0-0:3]DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate a general comprehension of fundamental knowledge in computer vision.
- 2.Demonstrate comprehension of modern research topics in computer vision.
- 3.Demonstrate comprehension of computer vision applications.
- 4.Recognize the application scope of current methods.
- 5.Demonstrate skills in oral/writing in computer vision community.
- AIAA 5027Deep Learning for Visual Intelligence: Trends and Challenges[3-0-0:3]DescriptionThis is a task-oriented yet interaction-based course, which aims to scrutinize the recent trends and challenges in visual intelligence tasks (high- and low-level 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate a comprehensive understanding of the basics in computer vision.
- 2.Catch up with the recent trends of deep learning for computer vision.
- 3.Identify the challenges and potentials of computer vision.
- 4.Foster good ways of paper reading and problem findings in computer vision research.
- 5.Critically challenge existing methods and explore new ways in computer vision.
- 6.Motivate interests in new research directions in computer vision, such as event camera-based vision.
- AIAA 6011Topics in Artificial Intelligence[1-4 credit(s)]DescriptionSelected topics in Artificial Intelligence (AI) of current interest of current interest in emerging areas and not covered by existing courses. May be repeated for credit if different topics are covered. May be graded by letter or P/F for different offerings.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Recognize the current research trend in AI.
- 2.Explain the theories and applications in the chosen topics.
- 3.Apply the methodologies and techniques to real problems in the chosen topics.
- AIAA 6021Topics in Machine Learning[3-0-0:3]DescriptionCovers emerging topics of machine learning. Potential topics include machine learning and cognitive science, transfer learning, multi-task learning, active learning, lifelong learning, assemble learning, and advances in deep learning. Graded P or F.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand of recent developments in a specific area of machine learning.
- AIAA 6090Research Internship[0 credit]DescriptionThe course will provide students with the opportunity to gain relevant knowledge, skills, and experience while establishing important connections in the field. Graded P or F.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Gain first-hand experience of AI application in the real-world.
- AIAA 6091Independent Study[3-0-0:3]DescriptionAn independent research project carried out under the supervision of a faculty member. Graded P or F.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate understanding of recent developments in a specific area of AI.
- AIAA 6101Artificial Intelligence Seminar I[0 credit]DescriptionSeries 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand current AI research trends and applications.
- AIAA 6102Artificial Intelligence Seminar II[0-1-0:1]DescriptionSeries 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate an understanding of current AI research trends and applications.
- AIAA 6990MPhil Thesis ResearchDescriptionMaster'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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Design, develop and conduct cross-disciplinary research in Artificial Intelligence.
- 2.Demonstrate practical skills in building AI systems.
- 3.Communicate research findings effectively in written and oral presentations.
- AIAA 7990Doctoral Thesis ResearchDescriptionOriginal 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Design, develop and conduct cross-disciplinary research in Artificial Intelligence.
- 2.Critically apply theories, methodologies and knowledge to address fundamental questions in AI.
- 3.Demonstrate practical skills in building AI systems.
- 4.Communicate research findings effectively in written and oral presentations.
