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]DescriptionThis 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 5025Deep Reinforcement Learning[3-0-0:3]DescriptionThis course covers recent developments in deep reinforcement learning. Topics include reinforcement learning basics, deep Q-learning, policy gradients, actor-critic algorithms, model-based reinforcement learning, imitation learning, inverse reinforcement learning, hierarchical reinforcement learning, and multi-agent reinforcement learning.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate an understanding of deep reinforcement learning as a field of study.
- 2.Demonstrate an understanding of the fundamental issues and principles in deep reinforcement learning.
- 3.Demonstrate an understanding of core and recent deep reinforcement learning algorithms.
- 4.Apply core and recent deep reinforcement learning algorithms to solve real-world problems.
- 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 5028Machine Learning on Graphs[3-0-0:3]BackgroundImplement machine learning and deep learning codes in Python; Basic probability theory and linear algebra.DescriptionThis course covers recent developments in machine learning on graph-structured data. Topics include network embedding, graph neural networks, knowledge graph embedding, generative models for graphs, scalable graph neural networks, explainable graph neural networks, and their applications.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate a comprehension of advanced knowledge of machine learning on graphs.
- 2.Demonstrate a comprehension of modern research topics in machine learning on graphs.
- 3.Demonstrate a comprehension of applications of graph learning methods.
- 4.Recognize the limitations of current methods and make improvements.
- 5.Apply programming and data analytic skills.
- AIAA 5029Programming for Vision Systems[3-0-0:3]DescriptionThis course is a hands-on introduction to the algorithms and programming for visual learning problems of the intelligent mobile systems, such as self-driving cars, in the era of industry 4.0. This course is highlighted by practical programming assignments and projects. This course will first introduce the basic principles of machine/deep learning and computer vision. Then, the specific visual learning tasks crucial for achieving intelligent mobile systems (e.g., object detection, semantic segmentation, optical flow, stereo/depth estimation, pose estimation, lane detection, traffic sign detection) will be covered. Finally, the latest development of novel sensor-based vision and AI-based augmented reality (AR) /metaverse technologies for intelligent mobile systems will also be introduced.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Clearly understand the basic principles and techniques for intelligent systems.
- 2.Master the fundamental programming skills for visual learning problems in intelligent systems.
- 3.Identify the challenges and potentials in visual learning of intelligent systems.
- 4.Lay a solid foundation for further research in visual learning of intelligent systems.
- 5.Master the skills of paper reading and problem findings for intelligent systems.
- AIAA 5030Foundations of Data Mining[3-0-0:3]DescriptionThis course will introduce the fundamental principles, uses, and technical details of data mining techniques by lectures and real-world case studies. The emphasis is on understanding the basic data mining techniques and their applications. We will discuss the mechanics of how data analytics techniques work as is necessary to understand the fundamental concepts and real-world applications.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate a comprehensive understanding of the basics in data mining.
- 2.Catch up with the current trends of data mining.
- 3.Demonstrate a comprehension of applications of data mining.
- 4.Identify the limitations of current methods and explore the improvements.
- 5.Critically apply data mining for business intelligence.
- AIAA 5031Introduction to Computing Using Python[3-0-0:3]BackgroundBasic programming skills, basic probability theory, linear algebra, and machine learning knowledge.DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Develop Python programs.
- 2.Apply Python for efficient mathematical calculation in scientific research.
- 3.Discover real-world phenomena and problems by processing and analyzing real-world data.
- 4.Develop AI models to address real-world applicational problems.
- 5.Comprehensively understand the use of scientific methods and techniques in AI.
- AIAA 5032Foundations of Artificial Intelligence[3-0-0:3]BackgroundProgramming in Python; Basic probability theory and linear algebra; Discrete MathDescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate a fundamental understanding of AI research and applications.
- 2.Understand the status of current AI research and applications, including their limitations and future potential.
- 3.Understand several common forms of inputs for AI research and applications.
- 4.Understand the core principles of AI research and experimental designs.
- 5.Demonstrate a comprehension of key algorithms and models in AI.
- 6.Design and develop small AI projects using the learned techniques.
- AIAA 5033AI Security and Privacy[3-0-0:3]BackgroundBasic knowledge of AI technologies, such as machine learning and federated learning; A bachelor’s degree in computer science or relevant disciplines.DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate a comprehension of security and privacy risks in AI technology.
- 2.Demonstrate a comprehension of data confidentiality protection technologies.
- 3.Demonstrate a comprehension of data integrity protection technologies.
- 4.Demonstrate a comprehension of availability ensuring technologies.
- 5.Recognize the limitations of current methods and make improvements.
- AIAA 5034Reinforcement Learning and Optimization[3-0-0:3]BackgroundBasic knowledge in machine learning, programming, and optimization.DescriptionLearning 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate fundamental understanding of reinforcement learning, such as Markov Decision Process, multi-armed bandits, Q-Learning, policy gradients, and imitation learning.
- 2.Address challenges in autonomous decision making by integrating scientific knowledge, technical applications, and innovative technology.
- 3.Identify and describe different scientific methods to critically evaluate complex, emerging decision-making problems for single agent and multi-agent scenarios.
- 4.Recognize the importance of exploration and exploitation to learn to make decisions under unknown environments.
- 5.Develop a broad interest in the environment and connect the knowledge to their major study.
- 6.Communicate effectively in written format and programming language to convey scientific knowledge and the application of machine learning techniques.
- 7.Apply the knowledge in solving engineering and technological challenges.
- AIAA 5036Autonomous AI[3-0-0:3]BackgroundProgramming in Python; Basic probability theory and linear algebra; Discrete MathDescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate a fundamental understanding of autonomous AI research and applications.
- 2.Understand the status of current autonomous AI research and applications, including their limitations and future potential.
- 3.Understand several common designs of autonomous AI systems.
- 4.Demonstrate a comprehension of key algorithms and models in autonomous AI.
- 5.Design and develop small AI projects using the learned techniques.
- AIAA 5037Advanced Algorithms and Data Structures[3-0-0:3]BackgroundBasic programming skillsDescriptionThis 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).Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate interest in designing more efficient algorithms to solve problems.
- 2.Understand typical fundamental and advanced algorithms.
- 3.Understand principles and usage of standard data structures.
- 4.Formulate real-world problems and design algorithms to solve them.
- 5.Know how to analyze the complexity of algorithms.
- 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[1-3 credit(s)]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.
